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Analog Evaluation

GerdIsenberg — Fri, 27 Oct 2017 08:04:25 GMT

Home * Hardware * Analog Evaluation

		Analog Evaluation, (Analogue Evaluation) a hypothetical dedicated special purpose hardware to evaluate a chess position using analog circuits such as resistive networks, operational amplifier (op-amps), and in particular the analogous FPGA counterparts FPAA, to map digital or discrete input signals, representing the board or aspects of the board, to an analog output representing an evaluation score along with some noise as input of an analog-to-digital converter. Jonathan Allen, Edward Hamilton, and Robert Levinson mentioned a method to convert a chess mobility graph to a resistive network which could be computed using the residual resistance property of interconnected chips within their Morph III project ^[1]. Using memristors, memistors, or even a kind of motorized potentiometers ^[2] as used in closed loop control and servomechanisms, would allow the implementation of physical neural networks as analog evaluation device with machine learning features ^[3].
Summing amplifier ^[4]

Summing Amplifier

A summing amplifier using an operational amplifier with feedback resistor Rf sums several (weighted by input resistors 1..n) voltages to an negated output ...

$V_{\text{out}} = -R_{\text{f}} \left( \frac{V_1}{R_1} + \frac{V_2}{R_2} + \cdots + \frac{V_n}{R_n} \right)$

... and may be used to implement a classical evaluation function as linear combination of independent features (F) and associated weights (W):

$\displaystyle Eval = \sum_{i=1}^{n} {F_i * W_i}$

Publications

Jonathan Allen, Edward Hamilton, Robert Levinson (1997). New Advances in Adaptive Pattern-Oriented Chess. Advances in Computer Chess 8
Amir Yazdanbakhsh, Renée St. Amant, Bradley Thwaites, Jongse Park, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug Burger (2014). Toward General-Purpose Code Acceleration with Analog Computation. ACM SIGARCH Computer Architecture News - ISCA '14, Vol. 42, No. 3, pdf

Forum Posts

Discussion of Special Purpose hardware for chess search by Rick Fadden, CCC, April 25, 2008
Re: Discussion of Special Purpose hardware for chess search by Gerd Isenberg, CCC, April 25, 2008
How can you create an analog sigmoid voltage transfer function (from simple parts)?, AskElektonics, Reddit, March 24, 2014

External Links

References

^ Jonathan Allen, Edward Hamilton, Robert Levinson (1997). New Advances in Adaptive Pattern-Oriented Chess. Advances in Computer Chess 8
^ Motorized potentiometers for high precision proportional controls
^ SciDAC Review - HARDWARE: Cortical Computing with Memristive Nanodevices
^ A circuit diagram of a Summing amplifier made using an operational amplifier, made by Inductiveload, January 26 2009, Wikimedia Commons
^ Rätsel - Wiikipedia.de (German) » Riddle from Wikipedia

What links here?

Page	Date Edited
Analog Evaluation	Oct 27, 2017
Arduino	Feb 21, 2018
Evaluation	Feb 1, 2018
Hardware	Jan 20, 2018
Neural Networks	Mar 12, 2018
PIC Microcontroller	Oct 27, 2017

Up one Level

Asymmetric evaluation

GerdIsenberg — Thu, 17 Nov 2016 09:02:03 GMT

Home * Evaluation * Asymmetric evaluation

		Some programs score certain terms differently for the side they are playing and for the opponent. Most often the big terms, like king safety, passed pawn evaluation or mobility are scaled in some way. Whilst it might be argued that this idea runs counter the definition of chess as a zero-sum game, it also allows greater freedom in shaping the program's playing style. Richard Lang's programs were famous for their asymmetry in evaluation and forward pruning ^[1] , which emphasis safety (rules like 'do not start a wild attack, but worry a lot if the opponent does so') ^[2] .
Henry Moore - Helmed Head N° 2 ^[3]

Publications

Ingo Althöfer (1992). On Asymmetries in Chess Programs. ICCA Journal, Vol. 15, No. 1
Javier Ros Padilla (1994). Estimating Asymmetry and Selectivity in Chess Programs. ICCA Journal, Vol. 17, No. 1 ^[4]

Forum Posts

1997 ...

asymmetry by Andrew Tridgell, rgcc, August 12, 1997 » KnightCap, Parity Pruning
Re: Junior's long lines: more data about this... by Don Dailey, CCC, December 26, 1997
Genius' asymmetric-search by example: TRY yourself by Thorsten Czub, rgcc, December 30, 1997
Asymetric king safety by Tom King, rgcc, March 13, 1999

2000 ...

Parity of depth level and evaluation by Claude Le Page, CCC, June 13, 2005

2010 ...

evaluating color agnostic terms by Alvaro Cardoso, CCC, October 15, 2013
Different eval for white/black by Matthew Lai, CCC, January 05, 2015
Asymmetrical evaluation by Laurie Tunnicliffe, CCC, May 24, 2016 » Odd-Even Effect
Symmetrical evaluation and endgame by Hamfer, OpenChess Forum, June 02, 2016 » Tempo

References

^ Genius' asymmetric-search by example: TRY yourself by Thorsten Czub, rgcc, December 30, 1997
^ Kaare Danielsen (1987). The 7th World Microcomputer Chess Championship, Rome, Italy, September 14-20, 1987. ICCA Journal, Vol. 10, No. 3 » WMCCC 1987
^ Henry Moore - Helmed Head N° 2 (1950), Bronze on Marble slab, Art Museum, Bochum, North Rhine-Westphalia, Germany, part of The Industrial Heritage Trail of the Ruhr area, Photo by Gerd Isenberg, October 30, 2016, see also 2011 image of the sculpture at Art Gallery of New South Wales from Wikimedia Commons
^ ICCA Journal, Vol. 17, No. 1 by Jos Uiterwijk, rgcc, May 02, 1994

What links here?

Page	Date Edited
Alvaro Cardoso	Jan 12, 2018
Andrew Tridgell	Aug 3, 2015
Asymmetric evaluation	Nov 17, 2016
Evaluation	Feb 1, 2018
Glass	May 24, 2017
Hossa	Jan 7, 2016
Javier Ros Padilla	Sep 2, 2015
KnightCap	Nov 6, 2016
Laurie Tunnicliffe	Jun 13, 2017
Matthew Lai	Dec 6, 2017
Mephisto Roma	Sep 1, 2017
Odd-Even Effect	May 19, 2017
Opponent Model Search	Aug 14, 2017
Parity Pruning	Jan 14, 2013
Psion	Mar 20, 2017
Richard Lang	Jan 7, 2016
Tempo	Jan 17, 2018
Tom King	Aug 19, 2017

Up one Level

Automated Tuning

GerdIsenberg — Tue, 27 Feb 2018 14:13:07 GMT

Home * Automated Tuning

		Automated Tuning, an automated adjustment of evaluation parameters or weights, and less commonly, search parameters ^[1], with the aim to improve the playing strength of a chess engine or game playing program. Evaluation tuning can be applied by mathematical optimization or machine learning, both fields with huge overlaps. Learning approaches are subdivided into supervised learning using labeled data, and reinforcement learning to learn from trying, facing the exploration (of uncharted territory) and exploitation (of current knowledge) dilemma. Johannes Fürnkranz gives a comprehensive overview in Machine Learning in Games: A Survey published in 2000 ^[2], covering evaluation tuning in chapter 4.
Engine Tuner ^[3]

Playing Strength

A difficulty in tuning and automated tuning of engine parameters is measuring playing strength. Using small sets of test-positions, which was quite common in former times to estimate relative strength of chess programs, lacks adequate diversity for a reliable strength predication. In particular, solving test-positions does not necessarily correlate with practical playing strength in matches against other opponents. Therefore, measuring strength requires to play many games against a reference opponent to determine the win rate with a certain confidence. The closer the strength of two opponents, the more games are necessary to determine whether changed parameters or weights in one of them are improvements or not, up to several tens of thousands. Playing many games with ultra short time controls has became de facto standard with todays strong programs, as for instance applied in Stockfish's Fishtest, using the sequential probability ratio test (SPRT) to possibly terminate a match early ^[4].

Parameter

Quote by Ingo Althöfer ^[5] ^[6]:

It is one of the best arts to find the right SMALL set of parameters and to tune them. Some 12 years ago I had a technical article on this ("On telescoping linear evaluation functions") in the ICCA Journal, Vol. 16, No. 2, pp. 91-94, describing a theorem (of existence) which says that in case of linear evaluation functions with lots of terms there is always a small subset of the terms such that this set with the right parameters is almost as good as the full evaluation function.

Mathematical Optimization

Mathematical optimization methods in tuning consider the engine as a black box.

Methods

Instances

Advantages

Works with all engine parameters, including search
Takes search-eval interaction into account

Disadvantages

Time complexity issues with increasing number of weights to tune

Reinforment Learning

Reinforcement learning, in particular temporal difference learning, has a long history in tuning evaluation weights in game programming, first seeen in the late 50s by Arthur Samuel in his Checkers player ^[7]. In self play against a stable copy of itself, after each move, the weights of the evaluation function were adjusted in a way that the score of the root position after a quiescence search became closer to the score of the full search. This TD method was generalized and formalized by Richard Sutton in 1988 ^[8], who introduced the decay parameter λ, where proportions of the score came from the outcome of Monte Carlo simulated games, tapering between bootstrapping (λ = 0) and Monte Carlo (λ = 1). TD-λ was famously applied by Gerald Tesauro in his Backgammon program TD-Gammon ^[9] ^[10], its minimax adaption TD-Leaf was successful used in eval tuning of chess programs ^[11], with KnightCap ^[12] and CilkChess ^[13] as prominent samples.

Instances

Engines

Supervised Learning

Move Adaption

One supervised learning method considers desired moves from a set of positions, likely from grandmaster games, and tries to adjust their evaluation weights so that for instance a one-ply search agrees with the desired move. Already pioneering in reinforcement learning some years before, move adaption was described by Arthur Samuel in 1967 as used in the second version of his checkers player ^[15], where a structure of stacked linear evaluation functions was trained by computing a correlation measure based on the number of times the feature rated an alternative move higher than the desired move played by an expert ^[16]. In chess, move adaption was first described by Thomas Nitsche in 1982 ^[17], and with some extensions by Tony Marsland in 1985 ^[18]. Eval Tuning in Deep Thought as mentioned by Feng-hsiung Hsu et al. in 1990 ^[19], and later published by Andreas Nowatzyk, is also based on an extended form of move adaption ^[20]. Jonathan Schaeffer's and Paul Lu's efforts to make Deep Thought's approach work for Chinook in 1990 failed ^[21] - nothing seemed to produce results that were as good than their hand-tuned effort ^[22].

Value Adaption

A second supervised learning approach used to tune evaluation weights is based on regression of the desired value, i.e. using the final outcome from huge sets of positions from quality games, or other information supplied by a supervisor, i.e. in form of annotations from position evaluation symbols. Often, value adaption is reinforced by determining an expected outcome by self play ^[23].

Advantages

Can modify any number of weights simultaneously - constant time complexity

Disadvantages

Requires a source for the labeled data
Can only be used for evaluation weights or anything else that can be labeled
Works not optimal when combined with search

Regression

Regression analysis is a statistical process with a substantial overlap with machine learning to predict the value of an Y variable (output), given known value pairs of the X and Y variables. Parameter estimation in regression analysis can be formulated as the minimization of a cost or loss function over a training set ^[24], such as mean squared error or cross-entropy error function for binary classification ^[25]. The minimization is implemented by iterative optimization algorithms or metaheuristics such as Iterated local search, Gauss–Newton algorithm, or conjugate gradient method.

Linear Regression

		The supervised problem of regression applied to move adaption was used by Thomas Nitsche in 1982, minimizing the mean squared error of a cost function considering the program’s and a grandmaster’s choice of moves, as mentioned, extended by Tony Marsland in 1985, and later by the Deep Thought team. Regression used to adapt desired values was described by Donald H. Mitchell in his 1984 masters thesis on evaluation features in Othello, cited by Michael Buro ^[26] ^[27]. Jens Christensen applied linear regression to chess in 1986 to learn point values in the domain of temporal difference learning ^[28].
Linear Regression ^[29]

Logistic Regression

		Since the relationship between win percentage and pawn advantage is assumed to follow a logistic model, one may treat static evaluation as single-layer perceptron or single neuron ANN with the common logistic activation function, performing the perceptron algorithm to train it ^[30]. Logistic regression in evaluation tuning was first elaborated by Michael Buro in 1995 ^[31], and proved successful in the game of Othello in comparison with Fisher's linear discriminant and quadratic discriminant function for normally distributed features, and served as eponym of his Othello program Logistello ^[32]. In computer chess, logistic regression was proposed by Miguel A. Ballicora in a 2009 CCC post, as applied to Gaviota ^[33], was independently described by Amir Ban in 2012 for Junior's evaluation learning ^[34], and explicitly mentioned by Álvaro Begué in a January 2014 CCC discussion ^[35], when Peter Österlund explained Texel's Tuning Method ^[36], which subsequently popularized logistic regression tuning in computer chess. Vladimir Medvedev's Point Value by Regression Analysis ^[37] ^[38] experiments showed why the logistic function is appropriate, and further used cross-entropy and regularization.
Logistic function ^[39]

Instances

Arasan's Tuning
Eval Tuning in Deep Thought
Minimax Tree Optimization (MMTO or the Bonanza-Method in Shogi)
Point Value by Regression Analysis
RuyTune
Texel's Tuning Method
Winter

Publications

1959

Arthur Samuel (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal July 1959

1960 ...

Arnold K. Griffith (1966). A new Machine-Learning Technique applied to the Game of Checkers. MIT, Project MAC, MAC-M-293
Arthur Samuel (1967). Some Studies in Machine Learning. Using the Game of Checkers. II-Recent Progress. pdf

1970 ...

Arnold K. Griffith (1974). A Comparison and Evaluation of Three Machine Learning Procedures as Applied to the Game of Checkers. Artificial Intelligence, Vol. 5, No. 2

1980 ...

Thomas Nitsche (1982). A Learning Chess Program. Advances in Computer Chess 3
Donald H. Mitchell (1984). Using Features to Evaluate Positions in Experts' and Novices' Othello Games. Masters thesis, Department of Psychology, Northwestern University, Evanston, IL

1985 ...

Tony Marsland (1985). Evaluation-Function Factors. ICCA Journal, Vol. 8, No. 2, pdf
Jens Christensen, Richard Korf (1986). A Unified Theory of Heuristic Evaluation functions and Its Applications to Learning. Proceedings of the AAAI-86, pp. 148-152, pdf
Jens Christensen (1986). Learning Static Evaluation Functions by Linear Regression. in Tom Mitchell, Jaime Carbonell, Ryszard Michalski (1986). Machine Learning: A Guide to Current Research. The Kluwer International Series in Engineering and Computer Science, Vol. 12
Dap Hartmann (1987). How to Extract Relevant Knowledge from Grandmaster Games. Part 1: Grandmasters have Insights - the Problem is what to Incorporate into Practical Problems. ICCA Journal, Vol. 10, No. 1
Dap Hartmann (1987). How to Extract Relevant Knowledge from Grandmaster Games. Part 2: the Notion of Mobility, and the Work of De Groot and Slater. ICCA Journal, Vol. 10, No. 2
Bruce Abramson, Richard Korf (1987). A Model of Two-Player Evaluation Functions. AAAI-87. pdf
Bruce Abramson (1988). Learning Expected-Outcome Evaluators in Chess. Proceedings of the 1988 AAAI Spring Symposium Series: Computer Game Playing, 26-28.
Kai-Fu Lee, Sanjoy Mahajan (1988). A Pattern Classification Approach to Evaluation Function Learning. Artificial Intelligence, Vol. 36, No. 1
Richard Sutton (1988). Learning to Predict by the Methods of Temporal Differences. Machine Learning, Vol. 3, No. 1, pdf
Bruce Abramson (1989). On Learning and Testing Evaluation Functions. Proceedings of the Sixth Israeli Conference on Artificial Intelligence, 1989, 7-16.
Maarten van der Meulen (1989). Weight Assessment in Evaluation Functions. Advances in Computer Chess 5

1990 ...

Bruce Abramson (1990). Expected-Outcome: A General Model of Static Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 2
Bruce Abramson (1990). An Analysis of Expected-Outcome. Journal of Experimental and Theoretical Artificial Intelligence 2: 55-73.
Bruce Abramson (1990). On Learning and Testing Evaluation Functions. Journal of Experimental and Theoretical Artificial Intelligence, Vol. 2
Feng-hsiung Hsu, Thomas Anantharaman, Murray Campbell, Andreas Nowatzyk (1990). A Grandmaster Chess Machine. Scientific American, Vol. 263, No. 4, pp. 44-50. ISSN 0036-8733.
Bruce Abramson (1991). The Expected-Outcome Model of Two-Player Games. Part of the series, Research Notes in Artificial Intelligence (San Mateo: Morgan Kaufmann, 1991).
Alex van Tiggelen (1991). Neural Networks as a Guide to Optimization - The Chess Middle Game Explored. ICCA Journal, Vol. 14, No. 3
William Tunstall-Pedoe (1991). Genetic Algorithms Optimizing Evaluation Functions. ICCA Journal, Vol. 14, No. 3
Paul E. Utgoff, Jeffery A. Clouse (1991). Two Kinds of Training Information for Evaluation Function Learning. University of Massachusetts, Amherst, Proceedings of the AAAI 1991
Gerald Tesauro (1992). Temporal Difference Learning of Backgammon Strategy. ML 1992
Ingo Althöfer (1993). On Telescoping Linear Evaluation Functions. ICCA Journal, Vol. 16, No. 2, pp. 91-94
Peter Mysliwietz (1994). Konstruktion und Optimierung von Bewertungsfunktionen beim Schach. Ph.D. thesis (German)

1995 ...

Michael Buro (1995). Statistical Feature Combination for the Evaluation of Game Positions. JAIR, Vol. 3
Chris McConnell (1995). Tuning Evaluation Functions for Search. ps or pdf from CiteSeerX
Chris McConnell (1995). Tuning Evaluation Functions for Search (Talk), ps
Johannes Fürnkranz (1996). Machine Learning in Computer Chess: The Next Generation. ICCA Journal, Vol. 19, No. 3, zipped ps
Don Beal, Martin C. Smith (1997). Learning Piece Values Using Temporal Differences. ICCA Journal, Vol. 20, No. 3
Thomas Anantharaman (1997). Evaluation Tuning for Computer Chess: Linear Discriminant Methods. ICCA Journal, Vol. 20, No. 4
Jonathan Baxter, Andrew Tridgell, Lex Weaver (1998). Experiments in Parameter Learning Using Temporal Differences. ICCA Journal, Vol. 21, No. 2, pdf
Michael Buro (1998). From Simple Features to Sophisticated Evaluation Functions. CG 1998, pdf
James C. Spall (1998). Implementation of the Simultaneous Perturbation Algorithm for Stochastic Optimization. IEEE Transactions on Aerospace and Electronic Systems, pdf ^[40]
Don Beal, Martin C. Smith (1999). Learning Piece-Square Values using Temporal Differences. ICCA Journal, Vol. 22, No. 4

2000 ...

Johannes Fürnkranz (2000). Machine Learning in Games: A Survey. Austrian Research Institute for Artificial Intelligence, OEFAI-TR-2000-3, pdf
Robert Levinson, Ryan Weber (2000). Chess Neighborhoods, Function Combination, and Reinforcement Learning. CG 2000, pdf
Johannes Fürnkranz, Miroslav Kubat (eds.) (2001). Machines that Learn to Play Games. Advances in Computation: Theory and Practice, Vol. 8,. NOVA Science Publishers
Gerald Tesauro (2001). Comparison Training of Chess Evaluation Functions. » SCP, Deep Blue
Graham Kendall, Glenn Whitwell (2001). An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics. Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 2, pdf
Yngvi Björnsson, Tony Marsland (2001). Learning Search Control in Adversary Games. Advances in Computer Games 9, pp. 157-174. pdf
Michael Buro (2002). Improving Mini-max Search by Supervised Learning. Artificial Intelligence, Vol. 134, No. 1, pdf
Dave Gomboc, Tony Marsland, Michael Buro (2003). Evaluation Function Tuning via Ordinal Correlation. Advances in Computer Games 10, pdf
Dave Gomboc (2004). Tuning Evaluation Functions by Maximizing Concordance. M.Sc. Thesis, University of Alberta
Adam Marczyk (2004). Genetic Algorithms and Evolutionary Computation from the TalkOrigins Archive
Petr Aksenov (2004). Genetic algorithms for optimising chess position scoring, Master's thesis, pdf
Mathieu Autonès, Aryel Beck, Phillippe Camacho, Nicolas Lassabe, Hervé Luga, François Scharffe (2004). Evaluation of Chess Position by Modular Neural network Generated by Genetic Algorithm. EuroGP 2004
Henk Mannen, Marco Wiering (2004). Learning to play chess using TD(λ)-learning with database games. Cognitive Artiﬁcial Intelligence, Utrecht University, Benelearn’04

2005 ...

Dave Gomboc, Michael Buro, Tony Marsland (2005). Tuning Evaluation Functions by Maximizing Concordance. Theoretical Computer Science, Vol. 349, No. 2, pdf
Jeff Rollason (2005). Evaluation by Hill-climbing: Getting the right move by solving micro-problems. AI Factory, Autumn 2005
Levente Kocsis, Csaba Szepesvári, Mark Winands (2005). RSPSA: Enhanced Parameter Optimization in Games. Advances in Computer Games 11, pdf

2006

Levente Kocsis, Csaba Szepesvári (2006). Universal Parameter Optimisation in Games Based on SPSA. Machine Learning, Special Issue on Machine Learning and Games, Vol. 63, No. 3
Hallam Nasreddine, Hendra Suhanto Po, Graham Kendall (2006). Using an Evolutionary Algorithm for the Tuning of a Chess Evaluation Function Based on a Dynamic Boundary Strategy. Proceedings of the 2006 IEEE Conference on Cybernetics and Intelligent Systems, pdf
Makoto Miwa, Daisaku Yokoyama, Takashi Chikayama (2006). Automatic Construction of Static Evaluation Functions for Computer Game Players. ALT ’06
Borko Bošković, Sašo Greiner, Janez Brest, Viljem Žumer (2006). A Differential Evolution for the Tuning of a Chess Evaluation Function. IEEE Congress on Evolutionary Computation, 2006.
Kunihito Hoki (2006). Optimal control of minimax search result to learn positional evaluation. 11th Game Programming Workshop (Japanese)

2007

Shogo Takeuchi, Tomoyuki Kaneko, Kazunori Yamaguchi, Satoru Kawai (2007). Visualization and Adjustment of Evaluation Functions Based on Evaluation Values and Win Probability. AAAI 2007, pdf
Makoto Miwa, Daisaku Yokoyama, Takashi Chikayama (2007). Automatic Generation of Evaluation Features for Computer Game Players. pdf
Johannes Fürnkranz (2007). Recent advances in machine learning and game playing. ÖGAI Journal, Vol. 26, No. 2, Computer Game Playing, pdf

2008

Omid David, Moshe Koppel, Nathan S. Netanyahu (2008). Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization. ACM Genetic and Evolutionary Computation Conference (GECCO '08), pp. 1469-1475, Atlanta, GA, July 2008.
Borko Bošković, Sašo Greiner, Janez Brest, Aleš Zamuda, Viljem Žumer (2008). An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program. Advances in Differential Evolution, Studies in Computational Intelligence, ISBN: 978-3-540-68827-3

2009

Joel Veness, David Silver, William Uther, Alan Blair (2009). Bootstrapping from Game Tree Search. Neural Information Processing Systems (NIPS), 2009, pdf
Omid David, Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu (2009). Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions. ACM Genetic and Evolutionary Computation Conference (GECCO '09), pp. 1483 - 1489, Montreal, Canada, July 2009.
Omid David (2009). Genetic Algorithms Based Learning for Evolving Intelligent Organisms. Ph.D. Thesis.
Broch Davison (2009). Playing Chess with Matlab. M.Sc. thesis supervised by Nello Cristianini, pdf ^[41]
Mark Levene, Trevor Fenner (2009). A Methodology for Learning Players' Styles from Game Records. arXiv:0904.2595v1
Wei-Lun Kao (2009). The Automatically Tuning System of Evaluation Function for Computer Chinese Chess. Master thesis, National Chiao Tung University, pdf (Chinese)

2010 ...

Amine Bourki, Matthieu Coulm, Philippe Rolet, Olivier Teytaud, Paul Vayssière (2010). Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing. pd
Omid David, Moshe Koppel, Nathan S. Netanyahu (2010). Genetic Algorithms for Automatic Search Tuning. ICGA Journal, Vol. 33, No. 2
Borko Bošković (2010). Differential evolution for the Tuning of a Chess Evaluation Function. Ph.D. thesis, University of Maribor

2011

Omid David, Moshe Koppel, Nathan S. Netanyahu (2011). Expert-Driven Genetic Algorithms for Simulating Evaluation Functions. Genetic Programming and Evolvable Machines, Vol. 12, No. 1
Borko Bošković, Janez Brest (2011). Tuning Chess Evaluation Function Parameters using Differential Evolution. Algorithm. Informatica, 35, No. 2, pdf
Borko Bošković, Janez Brest, Aleš Zamuda, Sašo Greiner, Viljem Žumer (2011). History mechanism supported differential evolution for chess evaluation function tuning. Soft Computing, Vol. 15, No. 4
Eduardo Vázquez-Fernández, Carlos Artemio Coello Coello, Feliú Davino Sagols Troncoso (2011). An Evolutionary Algorithm for Tuning a Chess Evaluation Function. CEC 2011, pdf
Eduardo Vázquez-Fernández, Carlos Artemio Coello Coello, Feliú Davino Sagols Troncoso (2011). An Adaptive Evolutionary Algorithm Based on Typical Chess Problems for Tuning a Chess Evaluation Function. GECCO 2011, pdf
Rémi Coulom (2011). CLOP: Confident Local Optimization for Noisy Black-Box Parameter Tuning. Advances in Computer Games 13 ^[42] ^[43]
Kunihito Hoki, Tomoyuki Kaneko (2011). The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search. Advances in Computer Games 13 » Shogi

2012

Amir Ban (2012). Automatic Learning of Evaluation, with Applications to Computer Chess. Discussion Paper 613, The Hebrew University of Jerusalem - Center for the Study of Rationality, Givat Ram
Kanjanapa Thitipong, Komiya Kanako, Yoshiyuki Kotani (2012). Design and Implementation of Bonanza Method for the Evaluation in the Game of Arimaa. IPSJ SIG Technical Report, Vol. 2012-GI-27, No. 4, pdf » Arimaa

2013

Wen-Jie Tseng, Jr-Chang Chen, I-Chen Wu, Ching-Hua Kuo, Bo-Han Lin (2013). A Supervised Learning Method for Chinese Chess Programs. JSAI2013, pdf
Akira Ura, Makoto Miwa, Yoshimasa Tsuruoka, Takashi Chikayama (2013). Comparison Training of Shogi Evaluation Functions with Self-Generated Training Positions and Moves. CG 2013, slides as pdf
Yoshikuni Sato, Makoto Miwa, Shogo Takeuchi, Daisuke Takahashi (2013). Optimizing Objective Function Parameters for Strength in Computer Game-Playing. AAAI 2013
Shalabh Bhatnagar, H. L. Prasad, L.A. Prashanth (2013). Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods. Lecture Notes in Control and Information Sciences, Vol. 434, Springer » SPSA
Tomáš Hřebejk (2013). Arimaa challenge - Static Evaluation Function. Master Thesis, Charles University in Prague, pdf » Arimaa ^[44]

2014

Kunihito Hoki, Tomoyuki Kaneko (2014). Large-Scale Optimization for Evaluation Functions with Minimax Search. JAIR Vol. 49, pdf » Shogi ^[45]
Aryan Mokhtari, Alejandro Ribeiro (2014). RES: Regularized Stochastic BFGS Algorithm. arXiv:1401.7625 ^[46]
Jemin Hwangbo, Christian Gehring, Hannes Sommer, Roland Siegwart, Jonas Buchli (2014). ROCK∗ — Efficient black-box optimization for policy learning. Humanoids, 2014 » Rockstar
James Martens (2014, 2017). New insights and perspectives on the natural gradient method. arXiv:1412.1193

2015 ...

Diederik P. Kingma, Jimmy Lei Ba (2015). Adam: A Method for Stochastic Optimization. arXiv:1412.6980v8, ICLR 2015 ^[47]
Aryan Mokhtari, Alejandro Ribeiro (2015). Global Convergence of Online Limited Memory BFGS. Journal of Machine Learning Research, Vol. 16, pdf ^[48] ^[49]

2017

Sebastian Ruder (2017). An overview of gradient descent optimization algorithms. arXiv:1609.04747v2 ^[50]

Forum Posts

1997 ...

Evolutionary Evaluation by Dan Homan, rgcc, September 09, 1997 » Evaluation
Deep Blue eval function tuning technique by Stuart Cracraft, CCC, January 08, 1998 » Deep Blue ^[51]
Automated Tuning by Stuart Cracraft, CCC, January 12, 1998
Pattern Matching -- Avoiding Hand-Tuning by Stuart Cracraft, CCC, January 21, 1998
Speaking of "Evaluate" by Danniel Corbit, CCC, September 29, 1998
Parameter Tuning by Jonathan Baxter, CCC, October 01, 1998 » TD-learning, KnightCap

2000 ...

Deep Thought's tuning code and eval function! by Severi Salminen, CCC, September 05, 2000 » Eval Tuning in Deep Thought
learning to tune parameters by comp-comp games by Uri Blass, CCC, December 28, 2000
Automatic Eval Tuning by Artem Pyatakov, CCC, June 29, 2001
deep blue's automatic tuning of evaluation function by Emerson Tan, CCC, March 22, 2003
evaluationfunction tuning by Jan Willem de Kort, CCC, September 07, 2003
evaluation tuning tricks by Peter Alloysius, CCC, March 17, 2004

2005 ...

"learning" or "tuning" programs by Sean Mintz, CCC, February 15, 2006
Adjusting weights the Deep Blue way by Tony van Roon-Werten, Winboard Forum, August 29, 2008 » Deep Blue
Tuning the eval by Daniel Anulliero, Winboard Forum, January 02, 2009
Insanity... or Tal style? by Miguel A. Ballicora, CCC, April 01, 2009
Re: Insanity... or Tal style? by Miguel A. Ballicora, CCC, April 02, 2009 ^[52]

2010 ...

Revisiting GA's for tuning evaluation weights by Ilari Pihlajisto, CCC, January 03, 2010
Idea for Automatic Calibration of Evaluation Function... by Steve Maughan, CCC, January 22, 2010
Re: TEST position TCEC5- Houdini 1.03a-DRybka4 1-0 by Milos Stanisavljevic, CCC, November 30, 2010
Parameter tuning by Onno Garms, CCC, March 13, 2011 » Onno
Ahhh... the holy grail of computer chess by Marcel van Kervinck, CCC, August 23, 2011
CLOP for Noisy Black-Box Parameter Optimization by Rémi Coulom, CCC, September 01, 2011 ^[53]
Tuning again by Ed Schroder, CCC, November 01, 2011
Ban: Automatic Learning of Evaluation [...] by BB+, OpenChess Forum, May 10, 2012 ^[54]

2014

How Do You Automatically Tune Your Evaluation Tables by Tom Likens, CCC, January 07, 2014
Re: How Do You Automatically Tune Your Evaluation Tables by Álvaro Begué, CCC, January 08, 2014
The texel evaluation function optimization algorithm by Peter Österlund, CCC, January 31, 2014 » Texel's Tuning Method
Re: The texel evaluation function optimization algorithm by Álvaro Begué, CCC, January 31, 2014 » Cross-entropy
Tuning eval by Daniel Anulliero, CCC, September 01, 2014
Tune cut margins with Texel/gaviota tuning method by Fabio Gobbato, CCC, September 11, 2014
Eval tuning - any open source engines with GA or PBIL? by Hrvoje Horvatic, CCC, December 04, 2014 » PBIL

2015 ...

MMTO for evaluation learning by Jon Dart, CCC, January 25, 2015 ^[55]
Experiments with eval tuning by Jon Dart, CCC, March 10, 2015 » Arasan, Texel's Tuning Method
txt: automated chess engine tuning by Alexandru Mosoi, CCC, March 18, 2015 » Zurichess, Texel's Tuning Method ^[56]
Re: txt: automated chess engine tuning by Sergei S. Markoff, CCC, February 15, 2016 » SmarThink
Piece weights with regression analysis (in Russian) by Vladimir Medvedev, CCC, April 30, 2015 » Point Value by Regression Analysis
Re: Piece weights with regression analysis (in Russian) by Fabien Letouzey, CCC, May 04, 2015
New Idea For Automated Tuning by Jordan Bray, CCC, May 16, 2015
Evaluation Tuning by Michael Hoffmann, CCC, August 09, 2015
Genetical tuning by Stefano Gemma, CCC, August 11, 2015 » Genetic Programming
Re: Genetical tuning by Ferdinand Mosca, CCC, August 20, 2015
Some musings about search by Ed Schroder, CCC, August 14, 2015 » Search
td-leaf by Alexandru Mosoi, CCC, October 06, 2015
tensorflow by Alexandru Mosoi, CCC, November 10, 2015 ^[57]

2016

pawn hash and eval tuning by J. Wesley Cleveland, CCC, February 21, 2016 » Pawn Hash Table
Tuning by ppyvabw, OpenChess Forum, June 11, 2016 » Texel's Tuning Method
GreKo 2015 ML: tuning evaluation (article in Russian) by Vladimir Medvedev, CCC, July 22, 2016 » GreKo, Texel's Tuning Method
A database for learning evaluation functions by Álvaro Begué, CCC, October 28, 2016 » Evaluation, Learning, Texel's Tuning Method
CLOP: when to stop? by Erin Dame, CCC, November 07, 2016 » CLOP
Re: CLOP: when to stop? by Álvaro Begué, CCC, November 08, 2016 ^[58]
C++ code for tuning evaluation function parameters by Álvaro Begué, CCC, November 10, 2016 » RuyTune ^[59]

2017

improved evaluation function by Alexandru Mosoi, CCC, March 11, 2017 » Texel's Tuning Method, Zurichess
automated tuning by Stuart Cracraft, CCC, March 13, 2017
Parameter tuning with multi objective optimization by Marco Pampaloni, CCC, May 07, 2017 » Napoleon
Evaluation Tuning: When To Stop? by Cheney Nattress, CCC, May 29, 2017
Texel tuning method question by Sander Maassen vd Brink, CCC, June 05, 2017 » Texel's Tuning Method
Re: Texel tuning method question by Peter Österlund, CCC, June 07, 2017
Re: Texel tuning method question by Ferdinand Mosca, CCC, July 20, 2017 » Python
Re: Texel tuning method question by Jon Dart, CCC, July 23, 2017
Approximating Stockfish's Evaluation by PSQTs by Thomas Dybdahl Ahle, CCC, August 23, 2017 » Regression, Piece-Square Tables, Stockfish
Ab-initio evaluation tuning by Evert Glebbeek, CCC, August 30, 2017
ROCK* black-box optimizer for chess by Jon Dart, CCC, August 31, 2017 » ROCK*, Rockstar
tuning via maximizing likelihood by Daniel Shawul, CCC, October 04, 2017 ^[60]
tool to create derivates of a given function by Alexandru Mosoi, CCC, November 07, 2017
Re: tool to create derivates of a given function by Daniel Shawul, CCC, November 07, 2017 ^[61]
tuning for the uninformed by Folkert van Heusden, CCC, November 23, 2017

2018

tuning info by Marco Belli, CCC, January 03, 2018
3 million games for training neural networks by Álvaro Begué, CCC, February 24, 2018 » Neural Networks

External Links

Optimization

Machine Learning

Statistics/Regression Analysis

Code

Misc

The Next Step Quintet feat. Tivon Pennicott - Regression, KerameioBar Athens, Greece, September 2014, YouTube Video

References

^ Yngvi Björnsson, Tony Marsland (2001). Learning Search Control in Adversary Games. Advances in Computer Games 9, pp. 157-174. pdf
^ Johannes Fürnkranz (2000). Machine Learning in Games: A Survey. Austrian Research Institute for Artificial Intelligence, OEFAI-TR-2000-3, pdf - Chapter 4, Evaluation Function Tuning
^ A vintage motor engine tester located at the James Hall museum of Transport, Johannesburg, South Africa - Engine tuning from Wikipedia
^ Fishtest Distributed Testing Framework by Marco Costalba, CCC, May 01, 2013
^ Re: Zappa Report by Ingo Althöfer, CCC, December 30, 2005 » Zappa
^ Ingo Althöfer (1993). On Telescoping Linear Evaluation Functions. ICCA Journal, Vol. 16, No. 2, pp. 91-94
^ Arthur Samuel (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal July 1959
^ Richard Sutton (1988). Learning to Predict by the Methods of Temporal Differences. Machine Learning, Vol. 3, No. 1, pdf
^ Gerald Tesauro (1992). Temporal Difference Learning of Backgammon Strategy. ML 1992
^ Gerald Tesauro (1994). TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play. Neural Computation Vol. 6, No. 2
^ Don Beal, Martin C. Smith (1999). Learning Piece-Square Values using Temporal Differences. ICCA Journal, Vol. 22, No. 4
^ Jonathan Baxter, Andrew Tridgell, Lex Weaver (1998). Experiments in Parameter Learning Using Temporal Differences. ICCA Journal, Vol. 21, No. 2, pdf
^ The Cilkchess Parallel Chess Program
^ EXchess also uses CLOP
^ Arthur Samuel (1967). Some Studies in Machine Learning. Using the Game of Checkers. II-Recent Progress. pdf
^ Johannes Fürnkranz (2000). Machine Learning in Games: A Survey. Austrian Research Institute for Artificial Intelligence, OEFAI-TR-2000-3, pdf
^ Thomas Nitsche (1982). A Learning Chess Program. Advances in Computer Chess 3
^ Tony Marsland (1985). Evaluation-Function Factors. ICCA Journal, Vol. 8, No. 2, pdf
^ Feng-hsiung Hsu, Thomas Anantharaman, Murray Campbell, Andreas Nowatzyk (1990). A Grandmaster Chess Machine. Scientific American, Vol. 263, No. 4, pp. 44-50. ISSN 0036-8733.
^ see 2.1 Learning from Desired Moves in Chess in Kunihito Hoki, Tomoyuki Kaneko (2014). Large-Scale Optimization for Evaluation Functions with Minimax Search. JAIR Vol. 49
^ Jonathan Schaeffer, Joe Culberson, Norman Treloar, Brent Knight, Paul Lu, Duane Szafron (1992). A World Championship Caliber Checkers Program. Artificial Intelligence, Vol. 53, Nos. 2-3,ps
^ Jonathan Schaeffer (1997, 2009). One Jump Ahead. 7. The Case for the Prosecution, pp. 111-114
^ Bruce Abramson (1990). Expected-Outcome: A General Model of Static Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 2
^ Loss function - Use in statistics - Wkipedia
^ "Using cross-entropy error function instead of sum of squares leads to faster training and improved generalization", from Sargur Srihari, Neural Network Training (pdf)
^ Michael Buro (1995). Statistical Feature Combination for the Evaluation of Game Positions. JAIR, Vol. 3
^ Donald H. Mitchell (1984). Using Features to Evaluate Positions in Experts' and Novices' Othello Games. Masters thesis, Department of Psychology, Northwestern University, Evanston, IL
^ Jens Christensen (1986). Learning Static Evaluation Functions by Linear Regression. in Tom Mitchell, Jaime Carbonell, Ryszard Michalski (1986). Machine Learning: A Guide to Current Research. The Kluwer International Series in Engineering and Computer Science, Vol. 12
^ Random data points and their linear regression. Created with Sage by Sewaqu, November 5, 2010, Wikimedia Commons
^ Re: Piece weights with regression analysis (in Russian) by Fabien Letouzey, CCC, May 04, 2015
^ Michael Buro (1995). Statistical Feature Combination for the Evaluation of Game Positions. JAIR, Vol. 3
^ LOGISTELLO's Homepage
^ Re: Insanity... or Tal style? by Miguel A. Ballicora, CCC, April 02, 2009
^ Amir Ban (2012). Automatic Learning of Evaluation, with Applications to Computer Chess. Discussion Paper 613, The Hebrew University of Jerusalem - Center for the Study of Rationality, Givat Ram
^ Re: How Do You Automatically Tune Your Evaluation Tables by Álvaro Begué, CCC, January 08, 2014
^ The texel evaluation function optimization algorithm by Peter Österlund, CCC, January 31, 2014
^ Определяем веса шахматных фигур регрессионным анализом / Хабрахабр by WinPooh, April 27, 2015 (Russian)
^ Piece weights with regression analysis (in Russian) by Vladimir Medvedev, CCC, April 30, 2015
^ log-linear 1 / (1 + 10^(-s/4)) , s=-10 to 10 from Wolfram|Alpha
^ SPSA Tuner for Stockfish Chess Engine by Joona Kiiski
^ MATLAB from Wikipedia
^ CLOP for Noisy Black-Box Parameter Optimization by Rémi Coulom, CCC, September 01, 2011
^ CLOP slides by Rémi Coulom, CCC, November 03, 2011
^ thesis on eval function learning in Arimaa by Jon Dart, CCC, December 04, 2015
^ MMTO for evaluation learning by Jon Dart, CCC, January 25, 2015
^ Broyden–Fletcher–Goldfarb–Shanno algorithm from Wikipedia
^ Arasan 19.2 by Jon Dart, CCC, November 03, 2016 » Arasan's Tuning
^ Limited-memory BFGS from Wikipedia
^ Re: CLOP: when to stop? by Álvaro Begué, CCC, November 08, 2016
^ Re: Texel tuning method question by Jon Dart, CCC, July 23, 2017
^ Thomas Anantharaman (1997). Evaluation Tuning for Computer Chess: Linear Discriminant Methods. ICCA Journal, Vol. 20, No. 4
^ The texel evaluation function optimization algorithm by Peter Österlund, CCC, January 31, 2014
^ Rémi Coulom (2011). CLOP: Confident Local Optimization for Noisy Black-Box Parameter Tuning. Advances in Computer Games 13
^ Amir Ban (2012). Automatic Learning of Evaluation, with Applications to Computer Chess. Discussion Paper 613, The Hebrew University of Jerusalem - Center for the Study of Rationality, Givat Ram
^ Kunihito Hoki, Tomoyuki Kaneko (2014). Large-Scale Optimization for Evaluation Functions with Minimax Search. JAIR Vol. 49, pdf
^ brtzsnr / txt — Bitbucket by Alexandru Mosoi
^ Home — TensorFlow
^ Limited-memory BFGS from Wikipedia
^ alonamaloh / ruy_tune — Bitbucket by Álvaro Begué
^ Maximum likelihood estimation from Wikipedia
^ Jacobian matrix and determinant from WIkipedia
^ Tool for automatic black-box parameter optimization released by Rémi Coulom, CCC, June 20, 2010
^ CLOP for Noisy Black-Box Parameter Optimization by Rémi Coulom, CCC, September 01, 2011
^ Re: CLOP: when to stop? by Álvaro Begué, CCC, November 08, 2016
^ Re: Eval tuning - any open source engines with GA or PBIL? by Jon Dart, CCC, December 06, 2014
^ Re: The texel evaluation function optimization algorithm by Jon Dart, CCC, March 12, 2014
^ Eval tuning - any open source engines with GA or PBIL? by Hrvoje Horvatic, CCC, December 04, 2014
^ ROCK* black-box optimizer for chess by Jon Dart, CCC, August 31, 2017

What links here?

Page	Date Edited
AI Factory	Dec 26, 2015
AICE	Nov 29, 2017
Akira Ura	Jun 26, 2017
Aldebaran	Feb 10, 2013
Alex van Tiggelen	May 4, 2013
Alexandru Mosoi	Jan 8, 2018
Alexs	Dec 7, 2017
Álvaro Begué	Feb 27, 2018
Amir Ban	Jan 24, 2016
Amoeba	Mar 19, 2018
Andreas Nowatzyk	Feb 6, 2014
Andscacs	Jan 16, 2018
Arasan	Apr 8, 2018
Arimaa	Jan 4, 2017
Artem Petakov	Jan 14, 2017
Arthur Samuel	May 29, 2016
Automated Tuning	Feb 27, 2018
Barbarossa	Dec 20, 2016
BBChess (SI)	Jan 12, 2015
Bo-Han Lin	Jul 22, 2017
Borko Bošković	May 23, 2016
Cheng	Jul 30, 2017
Chessterfield	Sep 29, 2016
Chimo	Jul 26, 2017
Chiron	Sep 24, 2017
Chris McConnell	Jan 7, 2016
CLOP	Nov 24, 2016
Cutechess-cli	Jan 6, 2018
Dan Homan	Oct 19, 2016
Daniel Anulliero	Oct 3, 2017
Dann Corbit	Dec 9, 2017
Dave Gomboc	May 6, 2015
Deep Blue	Aug 1, 2017
Deep Learning	Feb 12, 2018
Deep Sjeng	Jan 7, 2016
Ed Schroder	Feb 26, 2018
Eduardo Vázquez-Fernández	Aug 23, 2014
Engine Testing	Feb 1, 2018
Eval Tuning in Deep Thought	Jun 7, 2016
Evaluation	Feb 1, 2018
EXchess	Jan 21, 2018
Falcon	Sep 2, 2016
Feliú Davino Sagols Troncoso	May 17, 2014
Floyd	Sep 11, 2016
Folkert van Heusden	Nov 24, 2017
Fortress	Feb 1, 2018
Gaviota	Jan 21, 2018
Genetic Programming	Dec 26, 2017
Gerald Tesauro	Dec 13, 2016
Gerard Zielinski	Sep 25, 2014
Ginkgo	Jul 13, 2016
Golch	Dec 7, 2017
Graham Kendall	May 26, 2015
GullChess	Jun 27, 2017
Hagrid	Apr 12, 2016
Hannibal	Oct 4, 2017
Hedgehog	Feb 18, 2018
Home	Jul 2, 2018
iCE	Jan 4, 2016
ISA	Apr 2, 2018
Iteration	May 5, 2017
Janez Brest	May 23, 2016
Jeff Rollason	Dec 23, 2016
Jens Christensen	May 30, 2016
JikChess	Nov 29, 2015
Jon Dart	Dec 17, 2017
Jonathan Baxter	Feb 11, 2018
Joona Kiiski	Aug 31, 2017
Kai-Fu Lee	May 14, 2015
Kasparov versus Deep Blue 1997	Aug 1, 2017
KnightCap	Nov 6, 2016
Knowledge	Jul 22, 2017
Kokolo Ikeda	Mar 15, 2016
KRK	Nov 26, 2016
Kunihito Hoki	Jul 9, 2016
Laser	Dec 27, 2017
Learning	Feb 20, 2018
LearningLemming	Jun 29, 2013
Lyudmil Antonov	Aug 31, 2017
Makoto Miwa	Jun 26, 2017
Marco Belli	Jan 6, 2018
Match Statistics	Mar 31, 2018
Material	Mar 31, 2018
Mathematician	Apr 9, 2018
Matthias Lüscher	Sep 29, 2016
Maverick	Jan 7, 2016
Meep	Sep 4, 2016
Michael Buro	Dec 21, 2016
Michael Hoffmann	Aug 9, 2015
Michel Van den Bergh	Mar 10, 2018
Miguel A. Ballicora	Jun 19, 2017
Milos Stanisavljevic	May 23, 2016
Murka	Nov 11, 2016
Napoleon	Jun 25, 2017
Neural Networks	Mar 12, 2018
Nirvanachess	Sep 30, 2017
Onno	Jul 19, 2013
Onno Garms	Jul 19, 2013
Opponent Model Search	Aug 14, 2017
Optimization	Feb 28, 2018
Pawn Hash Table	Feb 1, 2018
Pedone	Jan 16, 2018
Peter Aloysius Harjanto	Jul 21, 2015
Peter Mysliwietz	Mar 5, 2017
Peter Österlund	Aug 24, 2017
Piece-Square Tables	Mar 31, 2018
Playing Strength	Mar 31, 2018
Point Value	Mar 31, 2018
Point Value by Regression Analysis	Aug 26, 2017
Pseudorandom number generator	May 11, 2017
QuTeChess	May 7, 2012
RattateChess	Dec 2, 2014
Rémi Coulom	Feb 5, 2018
Rhetoric	Nov 15, 2017
Rodent	Jan 11, 2018
RuyDos	Feb 17, 2018
RuyTune	Jun 8, 2017
Sanjoy Mahajan	May 14, 2015
Sašo Greiner	May 23, 2016
SCP	Jun 16, 2016
Search	Feb 1, 2018
Senpai	Nov 10, 2017
Sergei Markoff	Mar 12, 2018
Shay Bushinsky	Aug 28, 2016
Shogi	Feb 19, 2018
Simulated Annealing	May 8, 2017
SmarThink	Feb 2, 2018
space.menu	Oct 9, 2015
SPSA	May 8, 2017
Stefano Gemma	Dec 26, 2017
Stockfish	Apr 7, 2018
Stockfish's tuning method	May 21, 2017
Stoofvlees	Dec 7, 2017
Stuart Cracraft	May 19, 2017
Takuya Obata	Jan 7, 2017
Tao	Jan 8, 2016
Tapered Eval	Jan 9, 2018
Temporal Difference Learning	Feb 20, 2018
Test-Positions	Feb 25, 2018
Texel	Oct 9, 2017
Texel's Tuning Method	Dec 2, 2017
Thomas Dybdahl Ahle	Aug 26, 2017
Ti-Rong Wu	Jul 19, 2017
Tim Mann	Nov 5, 2016
Tom Likens	Mar 7, 2017
Tomoyuki Kaneko	Oct 25, 2017
Tony van Roon-Werten	Jan 14, 2017
Trial and Error	Feb 11, 2018
Wei-Lun Kao	Jul 22, 2017
William Tunstall-Pedoe	Apr 19, 2015
Winter	Jan 23, 2018
Yoshiyuki Kotani	Dec 27, 2016
Zugzwang (Program)	Dec 19, 2016
Zurichess	Mar 12, 2018
Stockfish : home	Feb 2, 2014

Up one Level

Backward Pawn

GerdIsenberg — Fri, 05 May 2017 10:30:24 GMT

Home * Evaluation * Pawn Structure * Backward Pawn

		A backward pawn is a pawn no longer defensible by own pawns and whose stop square lacks pawn protection but is controlled by a sentry. Thus, don't considering piece tactics, the backward pawn is not able to push forward without being lost, even establishing an opponent passer. If two opposing pawns on adjacent files in knight distance are mutually backward, the more advanced is not considered backward. A backward pawn is worse, or even a real Straggler, if on a half-open file as suitable target of opponent rooks. As pointed out by Sam Hamilton ^[1], considering stop squares might be insufficient for pawns which may actually push, but have a permanent weakened telestop. On the other hand, such a backward prospective pawn has a vital tempo, which is often decisive in certain pawn endings.
Behindhand Pawn ^[2]

Publications

Hans Kmoch (1959, 1990). Pawn Power in Chess. New York: Dover, 1990. Previous ed.: New York: McKay, 1959. ISBN 0-486-26486-6

Forum Posts

2000 ...

WHAT is the definition of a backward pawn? by Bas Hamstra, CCC, December 23, 2002
Backward Pawns by Mark Lefler, CCC, March 24, 2008
Doubled and Backward Pawn Engine "Definitions" by Brian Richardson, CCC, September 07, 2009
Re: Doubled and Backward Pawn Engine "Definitions" by Sam Hamilton, CCC, September 13, 2009

2010 ...

backward pawns in Stockfish by Marek Kwiatkowski, CCC, July 16, 2010
What is a backward pawn? by Lyudmil Tsvetkov, CCC, May 12, 2014
Pawn backwardness by Natale Galioto, CCC, October 06, 2015

External Links

Backward Pawn from Wikipedia

References

^ Re: Doubled and Backward Pawn Engine "Definitions" by Sam Hamilton, CCC, September 13, 2009
^ Chess painting in oil on canvas by Elke Rehder

What links here?

Page	Date Edited
Alarm	Jun 7, 2017
Backward Pawn	May 5, 2017
Backward Pawns (Bitboards)	May 5, 2017
Bas Hamstra	Feb 4, 2018
Blockage Detection	Oct 19, 2017
Cheese	Feb 17, 2018
Cheops (Miller)	Jan 13, 2017
ChessBrainVB	Jun 24, 2017
Clarabit	Nov 3, 2011
Coiled	Mar 12, 2017
Dartmouth CP	Jan 19, 2018
Defenchess	Mar 18, 2018
Diablo	May 17, 2016
Donna	Aug 17, 2017
Doubled Pawn	Mar 27, 2017
Evaluation function draft	Jul 11, 2014
Evaluation Overlap	Jul 19, 2016
Exacto	Oct 3, 2015
Faker	Nov 11, 2017
Garbochess	Sep 23, 2017
GopherCheck	Feb 3, 2017
Hakkapeliitta	Apr 26, 2016
Hans Kmoch	Nov 11, 2017
Hidden Passed Pawn	Nov 11, 2017
Holes	Oct 16, 2017
ISA	Apr 2, 2018
Isolated Pawn	Nov 18, 2012
Kaissa	Apr 9, 2018
King Pawn Tropism	Sep 1, 2014
Lyudmil Tsvetkov	Mar 28, 2018
Marek Kwiatkowski	Oct 22, 2017
Mark Lefler	Nov 23, 2017
Minority Attack	Jun 6, 2012
Nebula	Feb 7, 2015
NoonianChess	Jan 7, 2016
Open Pawns (Bitboards)	Aug 24, 2017
Pawn chain	Jan 17, 2018
Pawn Endgame	Oct 11, 2017
Pawn Majority	Jun 8, 2012
Pawn Rams (Bitboards)	Nov 11, 2017
Pawn Structure	Oct 2, 2017
Pawns and Files (Bitboards)	Jan 16, 2013
Prédateur	Nov 11, 2016
RattateChess	Dec 2, 2014
RuyDos	Feb 17, 2018
Sam Hamilton	Jan 28, 2015
Shield	May 17, 2017
Simplex	Mar 6, 2016
Sloppy	Sep 17, 2016
Stockfish	Apr 7, 2018
Tucano	Dec 16, 2017
Vajolet	Jan 25, 2018
Wasp	Nov 24, 2017
Weak Pawns	Dec 19, 2013
Wing	Oct 26, 2017
Winglet	Apr 26, 2015

Up one level

Bad bishop

GerdIsenberg — Mon, 22 Jun 2015 13:18:28 GMT

Home * Evaluation * Evaluation of Pieces * Bad bishop

Bad bishop is a bishop whose Mobility is restricted by own pawns. As opposed to normal mobility problems, this situation is semi-permanent (more so if the position is blocked), which fact justifies singling it out as another evaluation term. Bad bishop is not such a liability if it is placed outside the pawn chain (Bf5 in the Slav). For that reason some programs, like Crafty, score a bishop as bad only if its forward mobility is impaired.

For a bitboard-based program a feasible approach would be to create an array of 64 bitboards, indexed by a Bishop's position, and marking the pawn positions which would make our bishop bad.

Publications

Matej Guid, Martin Možina, Jana Krivec, Aleksander Sadikov, Ivan Bratko (2008). Learning Positional Features for Annotating Chess Games: A Case Study. CG 2008, pdf

Forum Posts

Evaluating bad bishops by Tord Romstad, Winboard Programming Forum, May 15, 2006
Bad/good bishops in R3 and IPPOLIT/IvanHoe by Mark Watkins, Open Chess Programming Forum, November 30, 2010

External Links

Good bishop and bad bishop from Wikipedia
How Bad is a Bad Bishop? by Iryna Zenyuk, February 27, 2019, Chess.com
The Bishow's Show: Bad Bishop by Iryna Zenyuk, March 09, 2012, Chess.com

What links here?

Page	Date Edited
Amundsen	Sep 3, 2013
Bad bishop	Jun 22, 2015
Bishops of Opposite Colors	Aug 22, 2012
Chester	Feb 15, 2014
Comet	Jan 8, 2016
Dreamer	Aug 5, 2015
Evaluation of Pieces	Jan 8, 2018
Exacto	Oct 3, 2015
Gambit Fruit	Mar 29, 2013
Middlegame	Sep 6, 2016
Murka	Nov 11, 2016
Pawn Rams (Bitboards)	Nov 11, 2017
Pawn Structure	Oct 2, 2017
PolarChess	May 15, 2017
Sayuri	Oct 1, 2017
Shield	May 17, 2017
Snitch	Apr 7, 2016
Zeus	Jan 3, 2018

Up one Level

Bishop Pair

GerdIsenberg — Sun, 10 Sep 2017 16:10:08 GMT

Home * Evaluation * of Pieces * Bishop Pair

Since in the initial position bishops stand on the squares of different color, throughout the game they control a disjoint sets of squares. This means that their power will never be wasted for carrying out the same tasks. On the other hand, lack of one bishop may introduce a color weakness. Taking all those considerations into account, one might conclude that possessing both bishops is a desirable thing. The question is, however, how much they are worth.

Larry Kaufman proposed the value of half a pawn ^[1]. It must be stressed, however, that this number is a part of a broader system, in which knights are stronger with many pawns on the board.

Publications

Gennady Timoshchenko (1993). Bishop or Knight? ICCA Journal, Vol. 16, No. 4
Mark Sturman (1995). Beware The Bishop Pair. Computer Chess Reports, Vol. 5, No. 2, pp.58 ^[2]
Mark Sturman (1996). Beware the Bishop Pair. ICCA Journal, Vol. 19, No. 2
Larry Kaufman (1999). The Evaluation of Material Imbalances. (first published in Chess Life March 1999, online version edited by Dan Heisman)

Forum Posts

Question about Crafty and Bishop Pair by Renze Steenhuisen, CCC, July 13, 2004 » Crafty
Testing of Kaufman Material Values by Mark Lefler, CCC, April 25, 2009 » Material
Rook Pair Penalty, Knight Pair Penalty, Having a Pawn Bonus by Mark Lefler, CCC, May 11, 2009

External Links

Bishop pair from Wikipedia

References

^ Larry Kaufman (1999). The Evaluation of Material Imbalances. (first published in Chess Life March 1999, online version edited by Dan Heisman)
^ Looking for Mark Sturman by Mark Lefler, rec.games.chess, July 02, 1995

What links here?

Page	Date Edited
Alarm	Jun 7, 2017
Battery	Jan 29, 2017
BigLion	Sep 28, 2016
Bishop Pair	Sep 10, 2017
Bishop versus knight	May 17, 2016
Bismark	Apr 22, 2015
chess22k	Apr 23, 2018
Coiled	Mar 12, 2017
Crafty	Jan 28, 2018
Demolito	Mar 1, 2018
Diablo	May 17, 2016
Djinn	Feb 8, 2016
Dorpsgek	Apr 2, 2018
Draw Evaluation	Mar 12, 2018
Evaluation of Pieces	Jan 8, 2018
Exacto	Oct 3, 2015
Fischerle	Aug 21, 2017
Garbochess	Sep 23, 2017
Gibbon	Dec 23, 2014
Gogobello	Jul 30, 2017
HeavyChess	Feb 14, 2014
ISA	Apr 2, 2018
Jan Renze Steenhuisen	Sep 10, 2017
Kaissa	Apr 9, 2018
Mark Sturman	May 17, 2016
Marvin	Feb 10, 2018
Material	Mar 31, 2018
Much	Jun 7, 2016
PolarChess	May 15, 2017
Quantifying Evaluation features	Jun 6, 2012
RattateChess	Dec 2, 2014
RuyDos	Feb 17, 2018
Sharper	Jun 6, 2017
Shield	May 17, 2017
Stockfish	Apr 7, 2018
Teki	Mar 29, 2018
Wing	Oct 26, 2017
Winglet	Apr 26, 2015
WyldChess	Mar 10, 2018
Xiphos	Apr 22, 2018

Up one Level

Bishop versus knight

GerdIsenberg — Tue, 17 May 2016 18:33:35 GMT

Endgame Considerations

Home * Evaluation * of Pieces * Bishop versus Knight

Bishop versus Knight,
is an often debated topic of the two pieces of similar relative strengths. Traditional thinking has been that the bishop is slighly better in open positions while the knight is better in closed positions ^[1]. However, the bishop pair versus knight and bishop or even more versus two knights yields an extra advantage as demonstrated in the winning percentages of the C, D, E rows of several material configurations 1 - 6 in Mark Sturman's table.

Endgame Considerations

In a minor piece endgame featuring bishop versus knight a couple of special considerations should be taken into account:

bishop becomes stronger if there are pawns on both wings
the side with a knight can defend by the means of blockade on the squares that are out of reach for the enemy bishop
if the side with a knight has a compact pawn mass, then it should place pawns on the squares controlled by enemy bishop to restrict its mobility.

Winning Percentages

of the bishop side as given by Mark Sturman, mining a database of over 350,000 games. Only results with at least 50 games have been recorded ^[2] :

		Piece Configurations	Number of pawns on each side
			1	2	3	4	5	6	7
1	A	B vs. N	51.2	50.6	50.3	49.5	48.6	45.7	43.6
	B	BN vs. NN	*	47.2	51.6	51.4	50.1	48.4	46.8
	C	BB vs. NB	58.2	65.9	66.1	67.6	65.2	62.4	58.1
	D	BB vs. NN	*	*	68.6	67.8	69.7	66.6	63.7
	E	BBN vs. NNB	*	*	66.9	64.3	63.6	62.1	59.1
2	A	BR vs. NR	48.6	51.5	51.3	51.4	50.7	48.8	46.6
	B	BNR vs. NNR	*	44.7	48.1	49.6	50.1	48.6	46.5
	C	BBR vs. NBR	*	62.7	64.0	64.9	63.8	61.6	60.8
	D	BBR vs. NNR	*	*	62.3	67.1	66.0	65.0	62.8
	E	BBNR vs. NNBR	*	*	62.0	63.5	62.3	58.1	56.6
3	A	BRR vs. NRR	*	48.3	50.4	51.2	50.1	48.6	46.2
	B	BNRR vs. NNRR	*	*	54.7	50.4	50.3	49.5	48.8
	C	BBRR vs. NBRR	*	*	62.3	63.0	61.9	59.1	56.0
	D	BBRR vs. NNRR	*	*	*	70.7	62.3	58.3	58.9
	E	BBNRR vs. NNBRR	*	*	61.2	61.9	60.5	56.0	53.9
4	A	BQ vs. NQ	*	49.7	49.9	48.2	48.9	47.8	45.4
	B	BNQ vs. NNQ	*	*	*	51.2	50.9	51.9	51.0
	C	BBQ vs. NBQ	*	*	55.6	60.5	59.3	58.7	54.5
	D	BBQ vs. NNQ	*	*	*	70.8	63.1	55.5	54.8
	E	BBNQ vs. NNBQ	*	*	*	56.4	56.9	56.6	59.5
5	A	BQR vs. NQR	*	53.6	50.9	50.8	51.1	50.3	48.4
	B	BNQR vs. NNQR	*	*	53.8	52.0	52.1	51.9	51.4
	C	BBQR vs. NBQR	*	*	58.0	57.4	58.2	57.1	55.5
	D	BBQR vs. NNQR	*	*	*	63.6	60.6	58.5	60.1
	E	BBNQR vs. NNBQR	*	*	69.6	57.9	56.1	54.7	56.5
6	A	BQRR vs. NQRR	*	*	53.0	51.8	51.1	49.3	48.4
	B	BNQRR vs. NNQRR	*	*	50.5	55.0	50.6	49.5	48.6
	C	BBQRR vs. NBQRR	*	*	62.5	58.8	57.0	54.8	53.1
	D	BBQRR vs. NNQRR	*	*	*	63.4	59.7	54.6	54.7
	E	BBNQRR vs. NNBQRR	*	*	60.2	57.0	55.0	53.0	52.3

* = Insufficient data

Publications

Gennady Timoshchenko (1993). Bishop or Knight? ICCA Journal, Vol. 16, No. 4
Mark Sturman (1995). Beware The Bishop Pair. Computer Chess Reports, Vol. 5, No. 2, pp.58 ^[3]
Mark Sturman (1996). Beware the Bishop Pair. ICCA Journal, Vol. 19, No. 2
Steve Mayer (1997). Bishop Versus Knight: The verdict. Batsford, amazon.com

References

^ Steve Mayer (1997). Bishop Versus Knight: The verdict. Batsford, amazon.com
^ Mark Sturman (1996). Beware the Bishop Pair. ICCA Journal, Vol. 19, No. 2, pp. 88
^ Looking for Mark Sturman by Mark Lefler, rec.games.chess, July 02, 1995

What links here?

Page	Date Edited
Bishop Pair	Sep 10, 2017
Bishop versus knight	May 17, 2016
Endgame	Sep 18, 2017
Evaluation of Pieces	Jan 8, 2018
Mark Sturman	May 17, 2016
Match Statistics	Mar 31, 2018
Pawn Advantage, Win Percentage, and Elo	Dec 20, 2017
Strategic Test Suite	Dec 19, 2016
Triangulation	Apr 4, 2013

Up one Level

Bishops of Opposite Colors

GerdIsenberg — Wed, 22 Aug 2012 15:59:25 GMT

Home * Evaluation * Game Phases * Endgame * Bishops of Opposite Colors

The endgame in which both sides posses only pawns and bishops of opposite colors is notorious difficult to win, as the weaker side is likely to create a blockade on the squares controlled by its own bishop. One pawn advantage is usually not enough to force the win. The chances for that exist either when the stronger side has either passed pawns on both wings or connected passed pawns. In the latter case the winning method is to advance pawns in such a manner that they are placed on the squares controlled by enemy bishop, since it makes blockade impossible.

Scaling down

A good idea for the evaluation function is to scale down the material value when the pure bishop of opposite colors ending is encountered. If some more pieces beside the bishops are present on the board, winning the endgame is easier, but requires complex strategy, based rather on zugzwang and attacking possibilities than on simply advancing passed pawns.

Forum Posts

Deep Thought.. What's this rules of Bishops? by Aravind Anumala, rgc, April 11, 1995
Hossa - Crafty : bishop endgame by Steffen A. Jakob, CCC, October 27, 1999 » Hossa, Crafty
Opposite Color Bishop Ending by Howard Exner, CCC, November 29, 1999
Test position (opposite colour bishop)..very hard for chess engines by Masros Tukiran, CCC, September 08, 2005

External Links

What links here?

Page	Date Edited
Bad bishop	Jun 22, 2015
Bishops of Opposite Colors	Aug 22, 2012
Color of a Square	May 24, 2015
Djinn	Feb 8, 2016
Endgame	Sep 18, 2017
Fortress	Feb 1, 2018
Gibbon	Dec 23, 2014
Hossa	Jan 7, 2016
Murka	Nov 11, 2016

Up one Level

Blockage Detection

GerdIsenberg — Thu, 19 Oct 2017 11:34:20 GMT

Home * Evaluation * Game Phases * Endgame * Pawn Endgame * Blockage Detection

4k3/8/3pPp2/1p1P1P1p/1P5P/5P2/3K4/8 w - -

Blockage Detection,
a pattern recognition technique to statically discover rammed positions in pawn endgames, and permanently prevented kings from penetrating into opponent's camp. In those positions material advantage is not sufficient to win such games, otherwise only recognizably by a very deep search, when a draw by fifty-move rule rises the horizon. Best not only applied in leaf node evaluation, but as interior node recognizer within the search, a detected blockade either prunes forward or narrows the search window.

Search Application

Ram Fence

Blockage with Dynamic Pawns

Search Application

Blockage detection is only applied for the side with advantage, proving its upper bound is a draw score ^[1]:

int search(int alpha, int beta, ...) {
  ...
  /* blockade detection */
  if ( beta > DRAW_SCORE ) {
    if ( blockage() ) {
      if ( alpha >= DRAW_SCORE )
        return DRAW_SCORE;
      beta = DRAW_SCORE; 
    }    
  }
  /* continue regular search */
}

Ram Fence

Following sample assumes White the "winning" side to move

The blockage requires a fence consisting of own fixed pawns and opponent pawn attacks, to divide the board into two disconnected regions. There are various algorithms to deal with fence detection, and fixed and dynamic pawns, following fill based proposal deals with bitboards and should only take none lever pawns into account to prove the fence is impenetrable. First cheap condition is the presence of at least three rams. Further own pawns blocked by own rams are included successively into the fence set, and finally opponent pawn attacks. The precondition, the defending king has at least one vacant square to avoid zugzwang issues, is omitted.

A king flood starts at its base rank (1st rank for White) over the board (ignoring black passers on the 2rd rank), or all bits below the fence' least significant one bit, the potential fence as flood stopping obstruction. If, after a few cycles, the flood reaches the above area, the fence is penetrable, and the blockage test failed. Each time, the king flood drowns (undefended) opponent pawns, the ram-fence is re-calculated with the drown pawns "captured". If the flood does not grow anymore, the fence is detected.

see One Step, Population Count and BitScan

bool forms_fence4white(U64 wPawns, U64 bPawns, U64 &fence, U64 &flood) {
  fence = wPawns & soutOne( bPawns ); 
  if (popCount( fence ) < 3)
    return false;
  fence |= wPawns & soutOne( fence );
  fence |= wPawns & soutOne( fence );
  fence |= wPawns & soutOne( fence );
  fence |= wPawns & soutOne( fence );
  fence |= bPawnAttacks( bPawns );
  flood = RANK1BB | allBitsBelow[BitScanForward( fence )];
  U64 above = allBitsAbove[BitScanReverse( fence )];
  while ( true ) {
    U64 temp = flood;
    flood |= eastOne( flood ) | westOne( flood ); /* Set all 8 neighbors */
    flood |= soutOne( flood ) | nortOne( flood );
    flood &= ~fence;
    if (flood == temp) 
      break; /* Fill has stopped, blockage? */
    if (flood & above) /* break through? */
      return false; /* yes, no blockage */
    if (flood & bPawns) {
      bPawns &= ~flood;  /* "capture" undefended black pawns */
      fence = wPawns & soutOne( bPawns ); 
      if (popCount( fence ) < 3)
        return false;
      fence |= wPawns & soutOne( fence );
      fence |= wPawns & soutOne( fence );
      fence |= wPawns & soutOne( fence );
      fence |= wPawns & soutOne( fence );
      fence |= bPawnAttacks( bPawns );
    }
  } 
  return true;
}

In order to ensure that White cannot penetrate the fence, the white kings must reside in the below flood set, and the defending, black king outside the below set. If all pawns of the "winning" side are rammed without any lever, the most obvious case of a blockage is recognized.

. . . k . . . .         a a a a a a a a
. . . . . . . .  above  a a a a a a a a
. . . . . . . .         a a a a a a a a
. b . . b . . b         a a a a a a a a
. w . . w . . w  fence  F_F_F_F_F_F_F_F
. w . . . . . .         b f b b b b b b
. . . K . . . .  below  b b b b b b b b
. . . . . . . .  flood  b b b b b b b b

Blockage with Dynamic Pawns

Otherwise, one has to consider dynamic pawns of the "winning" side, able to push forward or to capture, in particular above the fence, as member of the fence (levers) and below the fence (from White's point of view). For instance, one (protected) passed pawn, with the defending king on its front span, is not sufficient to break the blockage. Similar holds for pawns below the fence not able to lever, and becoming rammed if moving forward, or with some care, most backward pawns.

. . . . k . . .         a a a a a a a a
. . . . . . . .  above  a a a a a a a a
. . . b w b . .         a a a a a a a a
. b . w . w . b         a a F_F_F_F_F a
. w . . . . . w  fence  F_F_F b b b F_F
. . . . . w . .         b b b b b b b b
. . . K . . . .  below  b b b b b b b b
. . . . . . . .  flood  b b b b b b b b

More complicated cases for a successful blockage detection also with levers involved are elaborated in Omid David's et al. 2004 ICGA Journal paper ^[2], also published as code of Chiron by Ubaldo Andrea Farina in CCC ^[3].

Publications

Peter W. Frey, Larry Atkin (1979). Creating a Chess-Player, Part 4: Thoughts on Strategy. In Blaise W. Liffick (ed.), The Byte Book of Pascal, pp. 143-155. Byte Publications, also BYTE, Vol. 4, No. 1
Omid David, Ariel Felner, Nathan S. Netanyahu (2004). Blockage Detection in Pawn Endings. CG 2004, pdf
Omid David, Ariel Felner, Nathan S. Netanyahu (2004). Blockage Detection in Pawn Endgames. ICGA Journal, Vol. 27, No. 3

Forum Posts

Endgame Blockage Positions by Omid David, CCC, December 06, 2002
Jeremiah's Wall Detection by Renze Steenhuisen, CCC, April 28, 2005
Code of Chiron for Detection of Pawn Blockages by Ubaldo Andrea Farina, CCC, October 13, 2011
To users of Chiron by Richard Vida, CCC, January 02, 2012
Details about Critter 1.4a. by Jesús Muñoz, CCC, February 10, 2012 » Critter

References

^ Code snippet from Omid David, Ariel Felner, Nathan S. Netanyahu (2004). Blockage Detection in Pawn Endings. CG 2004, pdf
^ Omid David, Ariel Felner, Nathan S. Netanyahu (2004). Blockage Detection in Pawn Endgames. ICGA Journal, Vol. 27, No. 3
^ Code of Chiron for Detection of Pawn Blockages by Ubaldo Andrea Farina, CCC, October 13, 2011

What links here?

Page	Date Edited
BlackMamba	Nov 26, 2016
Blockade	Oct 19, 2017
Blockade of Stop	Oct 11, 2016
Blockage Detection	Oct 19, 2017
Chiron	Sep 24, 2017
Critter	Jan 25, 2014
Draw	Apr 14, 2018
Draw Evaluation	Mar 12, 2018
Endgame	Sep 18, 2017
Falcon	Sep 2, 2016
Fortress	Feb 1, 2018
Hossa	Jan 7, 2016
Interior Node Recognizer	Mar 12, 2018
Jan Renze Steenhuisen	Sep 10, 2017
Mate Search	Oct 22, 2016
Pawn Endgame	Oct 11, 2017
Pawn Rams (Bitboards)	Nov 11, 2017
Pawn Structure	Oct 2, 2017
Vajolet	Jan 25, 2018

Up one level

Candidate Passed Pawn

GerdIsenberg — Sat, 11 Nov 2017 09:32:23 GMT

Home * Evaluation * Pawn Structure * Candidate Passed Pawn

		Candidate passed pawn, a pawn on a half-open file, which, if the board had only pawns on it, would eventually become a passed pawn by moving forward. Whereas this definition is obvious for a human, in a form presented above it would require no less than a separate recursive search routine. For that reason, computers have to use approximations of that rule. One possibility is to define a pawn as a candidate, if no square on its path is controlled by more enemy pawns than own pawns. However, this simple heuristics rules out an early recognition of a candidate passed pawns. For example with white pawns on a4 and b5 and black pawn on a7, the b5 pawn would be viewed as a candidate passer only after a4-a5.
White and Black candidates (b5, g5)

Forum Posts

How to define a candidate passer by Jan Willem de Kort, CCC, January 03, 2006
Outside passer and candidate passer: definition wanted! :-) by Alessandro Scotti, CCC, February 27, 2006
Improving evaluation of passed pawns by Ben-Hur Carlos Vieira Langoni Junior, CCC, January 22, 2011 » Passed Pawn

External Links

References

^ Hans Kmoch (1959, 1990). Pawn Power in Chess. New York: Dover, 1990. Previous ed.: New York: McKay, 1959. ISBN 0-486-26486-6

What links here?

Page	Date Edited
More Links
Alessandro Scotti	Feb 27, 2014
Ben-Hur Carlos Vieira Langoni Junior	Dec 17, 2013
Bismark	Apr 22, 2015
Candidate Passed Pawn	Nov 11, 2017
Candidates (Bitboards)	Nov 11, 2017
Chexa	Feb 18, 2014
Clarabit	Nov 3, 2011
Coiled	Mar 12, 2017
Dartmouth CP	Jan 19, 2018
Dictionary	Aug 24, 2017
Doubled Pawn	Mar 27, 2017
Faker	Nov 11, 2017
Garbochess	Sep 23, 2017
Hans Kmoch	Nov 11, 2017
Hidden Passed Pawn	Nov 11, 2017
ISA	Apr 2, 2018
Nebula	Feb 7, 2015
Open Pawns (Bitboards)	Aug 24, 2017
Outside passed pawn	Jun 7, 2012
Passed Pawn	Oct 11, 2017

Up one Level

Center Control

GerdIsenberg — Wed, 17 Jan 2018 18:07:26 GMT

Home * Evaluation * Center Control

		Center Control, a chess strategy to control or occupy the center and extended center of the board, which gains space and allows pieces fast access of most of the board areas, and more movement and possibilities for attack and defense. In evaluation, center control is considered by piece-square tables and by mobility, where occupancy and controls are weighted accordantly. Chess openings try to control the center while developing pieces. Hypermodern openings are those that control the center with pieces from afar, such as with a fianchetto, Classical and Modern openings control it with pawns.
Kandinsky - Squares with Concentric Circles ^[1]

External Links

References

^ Wassily Kandinsky, Squares with Concentric Circles, 1913, Stadtische Galerie in Lenbach, Munich, Germany, Wikimedia Commons

What links here?

Page	Date Edited
BigLion	Sep 28, 2016
Center	Jun 21, 2016
Center Control	Jan 17, 2018
ChessBrainVB	Jun 24, 2017
Dan Heisman	Jun 12, 2013
Dartmouth CP	Jan 19, 2018
Development	Jan 17, 2018
Diablo	May 17, 2016
Dimitri	Aug 22, 2017
Djinn	Feb 8, 2016
Donna	Aug 17, 2017
Dragon FR	Sep 9, 2016
Evaluation	Feb 1, 2018
Eveann	Apr 3, 2017
Genie	Feb 7, 2016
Gk	Oct 9, 2017
Kaissa	Apr 9, 2018
King Centralization	Oct 30, 2017
LarsenVB	Jun 24, 2017
Master	Dec 28, 2017
Mephisto Vancouver	Sep 2, 2017
Mobility	Jan 17, 2018
Neurosis	Dec 23, 2017
Opening	Jun 25, 2017
Paul W. Abrahams	Oct 9, 2013
Pawn Center	Feb 12, 2015
Pawny	Jun 17, 2015
Point Value by Regression Analysis	Aug 26, 2017
Proscha	May 20, 2015
RattateChess	Dec 2, 2014
Ron Atkin	Oct 26, 2016
Senpai	Nov 10, 2017
Shura-Bura's Program	Sep 29, 2013
Simplex	Mar 6, 2016
SOMA	Mar 25, 2015
Space	Jan 18, 2018
Square Control	Sep 15, 2016
Strategic Test Suite	Dec 19, 2016
Strategy	Jan 18, 2018
SuperChess	Dec 7, 2016
Tempo	Jan 17, 2018
Vajolet	Jan 25, 2018

Up one level

Color Weakness

GerdIsenberg — Fri, 24 Nov 2017 10:01:57 GMT

Home * Evaluation * of Pieces * Color Weakness

Color weakness denotes a situation in which one of the players does not have sufficient control over the squares of a certain color. This is usually caused by the lack of a bishop of that color, coupled with the unfortunate pawn structure. In such situations, the opponent may use squares of that color to penetrate the opponent's position. Hans Kmoch defines insufficient occupation of the dark respectively light squares by pawns and bishop as black poverty or Melanpenie respectively white poverty or Leukopenie ^[1].

Examples

Typical examples of a color weakness are:

positions after the exchange of a fianchettoed bishop (this is especially painful when it was a defender of the castled king)
black exchanging a dark squared bishop in the French, the Slav or the Caro-Kann defences
white exchanging a light-squared bishop in the Queen's Gambit Declined or in the Colle
white exchanging a dark-squared bishop in the King's Indian, most notably in Sämisch

External Links

Color blindness from Wikipedia
Bob Berg & Mike Stern Band - Chromazone, YouTube Video
with Dennis Chambers and Lincoln Goines, The Bottom Line, New York City, February 25, 1990

References

^ Hans Kmoch (1959, 1990). Pawn Power in Chess. New York: Dover, 1990. ISBN 0-486-26486-6, Google Books

What links here?

Page	Date Edited
Bad bishop	Jun 22, 2015
Bishop Pair	Sep 10, 2017
Bishops of Opposite Colors	Aug 22, 2012
Color	Mar 12, 2014
Color Weakness	Nov 24, 2017
Evaluation of Pieces	Jan 8, 2018
Fianchetto	Feb 18, 2015
Gogobello	Jul 30, 2017
Hans Kmoch	Nov 11, 2017
Pawn Structure	Oct 2, 2017
Square Control	Sep 15, 2016

Up one Level

Connectivity

GerdIsenberg — Sun, 06 Mar 2016 09:05:23 GMT

Home * Evaluation * Connectivity

		Connectivity, in computer chess, a loosely defined property to quantify positional play based on the graph theoretical relationship between the chess pieces and the squares they control on the chessboard. Danny Kopec, Ed Northam, David Podber and Yehya Fouda proposed a connectivity term with some scaling required as sum of products of a value for each own protected pawn and own protected piece in the inverse order of their point value, i.e. {P=50, N=35, B=30, R=10, Q=4} times a value looked up by their up to three direct or x-ray protectors, i.e. {Single: P=8.00, B=4.50, N=4.00, R=3.00, K=2.50, Q=2.0; Double: PP=15.00, QK=4.00, ...; Triple: PPN=18.00, ...} ^[1]. This stems from the idea that the less mobile a piece is, the more protection it needs. The connectivity term encourages pawn chains, and discourages loose pieces. Originally, Kopec et al. used the computation of entropy, a term taken from physics, which measures the tendency of matter to increase in its level of disorder. A low entropy score was indicative of a high degree of protection and a high connectivity score.
Knight's graph ^[2]

Trajectories

More complex models concerning connectivity in chess, also considering progressive mobility, were proposed and elaborated by various researchers. To mention is Mikhail Botvinnik within his Pioneer project with the hierarchical mathematical projection (MP) of trajectories, zones consisting of a network of main trajectories conform to attacking or defending plans, opponent's counter trajectories and own supporting counter-counter trajectories ^[3].

Simplex and Complex

Ron Atkin and Ian H. Witten ^[4] labeled a piece and the squares that it attacks a simplex, and simplexes are composed into complexes. By analysis of the simplexes and complexes, Atkin and Witten showed that it is possible to make reasonably effective move predictions using real games as a point of comparison ^[5]. In line with Atkin and Witten, Derek Farren, Daniel Templeton and Meiji Wang proposed a model of four types of networks, support, mobility, position, and tracking networks, where certain incremental updated network properties, interpreted as a time series during the course of the game, may act as an evaluation feature ^[6].

Distance

Mobility as well as connectivity are special cases of the graph-theoretic representation of chess knowledge introduced by Robert Levinson and Richard Snyder, uniformly based on a single mathematical abstraction dubbed Distance, when squares are only at distance 1 ^[7].

Chess Neighborhood

Related to connectivity and distance is the graph-theoretic representation of Chess neighborhood, proposed by Robert Levinson and Ryan Weber, as applied in the learning research chess program Morph ^[8]. The chess neighborhood is a vector of 17 elements, focusing a square of the chessboard with 16 satellites corresponding to the closest pieces (if any) of the eight ray directions and pieces or empty squares in knight-distance of the eight knight directions.

A board representation as vector of 64 neighborhoods of each square is suited to serve as an input layer of a regression network, whose weights are optimized by gradient descent, along with training positions previously generated using temporal difference learning. At the lower level the expected probability of winning of the neighborhoods are learned and at the top they are combined based on their product and entropy.

The overall model of the Morph learner ^[9]

Publications

1970 ...

Ron Atkin (1972). Multi-Dimensional Structure in the Game of Chess. In International Journal of Man-Machine Studies, Vol. 4
Ron Atkin, Ian H. Witten (1975). A Multi-Dimensional Approach to Positional Chess. International Journal of Man-Machine Studies, Vol. 7, No. 6
Ron Atkin (1977). Positional Play in Chess by Computer. Advances in Computer Chess 1 ^[10]

1980 ...

Mikhail Botvinnik (1984). Computers in Chess: Solving Inexact Search Problems. Springer-Verlag, New York
Michael Tsfasman, Boris Stilman (1984). The Positional Estimate and Assignment of Priorities.
Yehya Fouda (1987). Computer Chess: A Study of Entropy and Opening Play. Master's thesis project report. San Diego State University
Danny Kopec, Ed Northam, David Podber, Yehya Fouda (1989). The Role of Connectivity in Chess. Workshop on New Directions in Game-Tree Search, pdf

1990 ...

Robert Levinson, Richard Snyder (1993). Distance: Toward the Unification of Chess Knowledge. ICCA Journal, Vol. 16, No. 3
Jonathan Allen, Edward Hamilton, Robert Levinson (1997). New Advances in Adaptive Pattern-Oriented Chess. Advances in Computer Chess 8

2000 ...

Boris Stilman (2000). Linguistic Geometry - From Search to Construction. Operations Research/Computer Science Interfaces Series. Springer US, ISBN: 978-0-7923-7738-2, Acknowledgments
Robert Levinson, Ryan Weber (2000). Chess Neighborhoods, Function Combination, and Reinforcement Learning. CG 2000, pdf
Daniel Walker, Robert Levinson (2004). The MORPH Project in 2004. ICGA Journal, Vol. 27, No. 4

2010 ...

Derek Farren, Daniel Templeton, Meiji Wang (2013). Analysis of Networks in Chess. Team 23, Stanford University, pdf

Forum Posts

LCP by Lyudmil Tsvetkov, CCC, February 09, 2015 » Pawn chain
Pawn defence by Lyudmil Tsvetkov, CCC, February 18, 2015

External Links

References

^ Danny Kopec, Ed Northam, David Podber, Yehya Fouda (1989). The Role of Connectivity in Chess. Workshop on New Directions in Game-Tree Search, pdf
^ Knight's graph showing the number of possible moves from each node, by R. A. Nonenmacher, June 23, 2008, Knight's tour from Wikipedia
^ Mikhail Botvinnik (1984). Computers in Chess: Solving Inexact Search Problems. Springer-Verlag, New York
^ Ron Atkin, Ian H. Witten (1975). A Multi-Dimensional Approach to Positional Chess. International Journal of Man-Machine Studies, Vol. 7, No. 6
^ Derek Farren, Daniel Templeton, Meiji Wang (2013). Analysis of Networks in Chess. Team 23, Stanford University, pdf
^ Derek Farren, Daniel Templeton, Meiji Wang (2013). Analysis of Networks in Chess. Team 23, Stanford University, pdf
^ Robert Levinson, Richard Snyder (1993). Distance: Toward the Unification of Chess Knowledge. ICCA Journal, Vol. 16, No. 3
^ Robert Levinson, Ryan Weber (2000). Chess Neighborhoods, Function Combination, and Reinforcement Learning. CG 2000, pdf
^ Image Fig. 5.3 on pg. 13 from Robert Levinson, Ryan Weber (2000). Chess Neighborhoods, Function Combination, and Reinforcement Learning. CG 2000, pdf
^ Re: Whatever happened to Neural Network Chess programs? by Andy Walker, rgcc, March 28, 2000

What links here?

Page	Date Edited
Alarm	Jun 7, 2017
BigLion	Sep 28, 2016
Chenard	Sep 5, 2015
Connectivity	Mar 6, 2016
Daniel Templeton	Jan 13, 2015
Daniel Walker	Sep 28, 2015
David Podber	Jan 5, 2015
Derek Farren	Jan 3, 2015
Distance	Sep 25, 2015
Evaluation	Feb 1, 2018
Loose Piece	Jan 10, 2015
Lyudmil Tsvetkov	Mar 28, 2018
Meiji Wang	Jan 13, 2015
Mobility	Jan 17, 2018
Morph	Dec 7, 2017
Pawn chain	Jan 17, 2018
Ron Atkin	Oct 26, 2016
Ryan Weber	Sep 25, 2015
Shield	May 17, 2017
Simplex	Mar 6, 2016
Square Control	Sep 15, 2016
Texel	Oct 9, 2017
Trajectory	Jan 3, 2015
Turochamp	Dec 14, 2017
Yehya Fouda	Jan 5, 2015

Up one level

CPW-Engine_eval

neurocore — Wed, 27 Jul 2016 04:38:57 GMT

Description

Code

What links here?

Home * Engines * CPW-Engine * Eval

Description

Using this evaluation function CPW-Engine should be aware of the following principles:
1) develop the pieces
2) castle if possible
3) need some minimal level of assistance to disrupt its own castling position
4) care for doubled, weak and passed pawns
5) know a thing or two about the changes of piece values as pawns dissappear
6) try to attack squares around enemy king

For function definitions, see CPW-Engine_eval_h

Code

#include "stdafx.h"
#include "0x88_math.h"
#include "eval.h"
#include "transposition.h"
 
/* adjustements of piece value based on the number of own pawns */
int knight_adj[9] = { -20, -16, -12, -8, -4,  0,  4,  8, 12};
int rook_adj[9] =   {  15,  12,   9,  6,  3,  0, -3, -6, -9};
 
static const int SafetyTable[100] = {
    0,  0,   1,   2,   3,   5,   7,   9,  12,  15,
  18,  22,  26,  30,  35,  39,  44,  50,  56,  62,
  68,  75,  82,  85,  89,  97, 105, 113, 122, 131,
 140, 150, 169, 180, 191, 202, 213, 225, 237, 248,
 260, 272, 283, 295, 307, 319, 330, 342, 354, 366,
 377, 389, 401, 412, 424, 436, 448, 459, 471, 483,
 494, 500, 500, 500, 500, 500, 500, 500, 500, 500,
 500, 500, 500, 500, 500, 500, 500, 500, 500, 500,
 500, 500, 500, 500, 500, 500, 500, 500, 500, 500,
 500, 500, 500, 500, 500, 500, 500, 500, 500, 500
};
 
/*******************************************************************
*  This struct holds data about certain aspects of evaluation,     *
*  which allows program to print them if desired.                  *
*******************************************************************/
 
struct eval_vector {
int gamePhase;
int mgMob[2];
int egMob[2];
int attCnt[2];
int attWeight[2];
int kingShield[2];
int MaterialAdjustement[2];
int Blockages[2];
int PositionalThemes[2];
} v;
 
int eval( int alpha, int beta, int use_hash ) {
    int result = 0, mgScore = 0, egScore = 0;
    int stronger, weaker;
 
    /***********************************************************
    /  Probe the evaluatinon hashtable, unless we call eval()  /
    /  only in order to display detailed result                /
    ***********************************************************/
 
    int probeval = tteval_probe();
    if (probeval != INVALID && use_hash)
        return probeval;
 
    /***********************************************************
    /  Clear all eval data                                     /
    ***********************************************************/
 
    v.gamePhase = 0;
    v.mgMob[WHITE] = 0;      v.mgMob[BLACK] = 0;
    v.egMob[WHITE] = 0;      v.egMob[BLACK] = 0;
    v.attCnt[WHITE] = 0;     v.attCnt[BLACK] = 0;
    v.attWeight[WHITE] = 0;  v.attWeight[BLACK] = 0;
    v.MaterialAdjustement[WHITE] = 0; v.MaterialAdjustement[BLACK] = 0;
    v.Blockages[WHITE] = 0; v.Blockages[BLACK] = 0;
    v.PositionalThemes[WHITE] = 0; v.PositionalThemes[BLACK] = 0;
    v.kingShield[WHITE] = 0; v.kingShield[BLACK] = 0;
 
    /* sum the incrementally counted material and pcsq values */
    mgScore = b.PieceMaterial[WHITE] + b.PawnMaterial[WHITE] + b.PcsqMg[WHITE]
            - b.PieceMaterial[BLACK] - b.PawnMaterial[BLACK] - b.PcsqMg[BLACK];
    egScore = b.PieceMaterial[WHITE] + b.PawnMaterial[WHITE] + b.PcsqEg[WHITE]
            - b.PieceMaterial[BLACK] - b.PawnMaterial[BLACK] - b.PcsqEg[BLACK];
 
    /* add king's pawn shield score and evaluate part of piece blockage score
    (the rest of the latter will be done via piece eval) */
    v.kingShield[WHITE] = wKingShield();
    v.kingShield[BLACK] = bKingShield();
    blockedPieces();
    mgScore += (v.kingShield[WHITE] - v.kingShield[BLACK]);
 
    /* tempo bonus */
    if ( b.stm == WHITE ) result += e.TEMPO;
    else                  result -= e.TEMPO;
 
    /*******************************************************************
    * Adjusting material value for the various combinations of pieces. *
    * Currently it scores bishop, knight and rook pairs. The first one *
    * gets a bonus, the latter two - a penalty. Please also note that  *
    * adjustements of knight and rook value based on the number of own *
    * pawns on the board are done within the piece-specific routines.  *
    *******************************************************************/
 
     if ( b.PieceCount[WHITE][BISHOP] > 1 ) result += e.BISHOP_PAIR;
     if ( b.PieceCount[BLACK][BISHOP] > 1 ) result -= e.BISHOP_PAIR;
     if ( b.PieceCount[WHITE][KNIGHT] > 1 ) result -= e.P_KNIGHT_PAIR;
     if ( b.PieceCount[BLACK][KNIGHT] > 1 ) result += e.P_KNIGHT_PAIR;
     if ( b.PieceCount[WHITE]  [ROOK] > 1 ) result -= e.P_ROOK_PAIR;
     if ( b.PieceCount[BLACK]  [ROOK] > 1 ) result += e.P_ROOK_PAIR;
 
     result += getPawnScore();
 
    /*******************************************************************
    *  Evaluate pieces                                                 *
    *******************************************************************/
 
   for (U8 row=0; row < 8; row++)
   for (U8 col=0; col < 8; col++) {
 
       S8 sq = SET_SQ(row, col);
 
       if ( b.color[sq] != COLOR_EMPTY ) {
           switch (b.pieces[sq]) {
           case PAWN: // pawns are evaluated separately
           break;
           case KNIGHT:
              EvalKnight(sq, b.color[sq]);
              break;
           case BISHOP:
              EvalBishop(sq, b.color[sq]);
              break;
           case ROOK:
               EvalRook(sq, b.color[sq]);
               break;
           case QUEEN:
               EvalQueen(sq, b.color[sq]);
               break;
           case KING:
               break;
       }
     }
   }
 
   /********************************************************************
   *  Merge midgame and endgame score. We interpolate between these    *
   *  two values, using a gamePhase value, based on remaining piece    *
   *  material on both sides. With less pieces, endgame score beco-    *
   *  mes more influential.                                            *
   ********************************************************************/
 
   mgScore += (v.mgMob[WHITE] - v.mgMob[BLACK]);
   egScore += (v.egMob[WHITE] - v.egMob[BLACK]);
   if (v.gamePhase > 24) v.gamePhase = 24;
   int mgWeight = v.gamePhase;
   int egWeight = 24 - mgWeight;
   result += ( (mgScore * mgWeight) + (egScore * egWeight) ) / 24;
 
   /********************************************************************
   *  Add phase-independent score components.                          *
   ********************************************************************/
 
   result += (v.Blockages[WHITE] - v.Blockages[BLACK]);
   result += (v.PositionalThemes[WHITE] - v.PositionalThemes[BLACK]);
   result += (v.MaterialAdjustement[WHITE] - v.MaterialAdjustement[BLACK]);
 
   /********************************************************************
    *  Merge king attack score. We don't apply this value if there are *
    *  less than two attackers or if the attacker has no queen.        *
    *******************************************************************/
 
   if (v.attCnt[WHITE] < 2 || b.PieceCount[WHITE][QUEEN] == 0) v.attWeight[WHITE] = 0;
   if (v.attCnt[BLACK] < 2 || b.PieceCount[BLACK][QUEEN] == 0) v.attWeight[BLACK] = 0;
   result += SafetyTable[v.attWeight[WHITE]];
   result -= SafetyTable[v.attWeight[BLACK]];
 
   /********************************************************************
   *  Low material correction - guarding against an illusory material  *
   *  advantage. Full blown program should have more such rules,  but  *
   *  the current set ought to be useful enough. Please note that our  *
   *  code  assumes different material values for bishop and  knight.  *
   *                                                                   *
   *  - a single minor piece cannot win                                *
   *  - two knights cannot checkmate bare king                         *
   *  - bare rook vs minor piece is drawish                            *
   *  - rook and minor vs rook is drawish                              *
   ********************************************************************/
 
   if (result > 0) { stronger = WHITE; weaker = BLACK; }
   else               { stronger = BLACK; weaker = WHITE; }
 
   if (b.PawnMaterial[stronger] == 0) {
 
       if (b.PieceMaterial[stronger] < 400) return 0;
 
       if (b.PawnMaterial[weaker] == 0
           && (b.PieceMaterial[stronger] == 2 * e.PIECE_VALUE[KNIGHT]))
           return 0;
 
       if (b.PieceMaterial[stronger] == e.PIECE_VALUE[ROOK]
           && b.PieceMaterial[weaker] == e.PIECE_VALUE[BISHOP]) result /= 2;
 
       if (b.PieceMaterial[stronger] == e.PIECE_VALUE[ROOK]
           && b.PieceMaterial[weaker] == e.PIECE_VALUE[KNIGHT]) result /= 2;
 
       if (b.PieceMaterial[stronger] == e.PIECE_VALUE[ROOK] + e.PIECE_VALUE[BISHOP]
           && b.PieceMaterial[stronger] == e.PIECE_VALUE[ROOK]) result /= 2;
 
       if (b.PieceMaterial[stronger] == e.PIECE_VALUE[ROOK] + e.PIECE_VALUE[KNIGHT]
           && b.PieceMaterial[stronger] == e.PIECE_VALUE[ROOK]) result /= 2;
   }
 
    /*******************************************************************
    *  Finally return the score relative to the side to move.          *
    *******************************************************************/
 
    if ( b.stm == BLACK ) result = -result;
 
    tteval_save(result);
 
    return result;
}
 
void EvalKnight(S8 sq, S8 side) {
     int att = 0;
     int mob = 0;
     int pos;
      v.gamePhase += 1;
 
     if (side == WHITE) {
         switch (sq) {
         case A8: if (isPiece(BLACK, PAWN, A7) || isPiece(BLACK, PAWN, C7)) v.Blockages[WHITE] -= e.P_KNIGHT_TRAPPED_A8; break;
         case H8: if (isPiece(BLACK, PAWN, H7) || isPiece(BLACK, PAWN, F7)) v.Blockages[WHITE] -= e.P_KNIGHT_TRAPPED_A8; break;
         case A7: if (isPiece(BLACK, PAWN, A6) && isPiece(BLACK, PAWN, B7)) v.Blockages[WHITE] -= e.P_KNIGHT_TRAPPED_A7; break;
         case H7: if (isPiece(BLACK, PAWN, H6) && isPiece(BLACK, PAWN, G7)) v.Blockages[WHITE] -= e.P_KNIGHT_TRAPPED_A7; break;
         case C3: if (isPiece(WHITE, PAWN, C2) && isPiece(WHITE, PAWN, D4) && !isPiece(WHITE, PAWN, E4)) v.Blockages[WHITE] -= e.P_C3_KNIGHT; break;
         }
     }
     else
     {
         switch (sq) {
         case A1: if (isPiece(WHITE, PAWN, A2) || isPiece(WHITE, PAWN, C2)) v.Blockages[BLACK] -= e.P_KNIGHT_TRAPPED_A8; break;
         case H1: if (isPiece(WHITE, PAWN, H2) || isPiece(WHITE, PAWN, F2)) v.Blockages[BLACK] -= e.P_KNIGHT_TRAPPED_A8; break;
         case A2: if (isPiece(WHITE, PAWN, A3) && isPiece(WHITE, PAWN, B2)) v.Blockages[BLACK] -= e.P_KNIGHT_TRAPPED_A7; break;
         case H2: if (isPiece(WHITE, PAWN, H3) && isPiece(WHITE, PAWN, G2)) v.Blockages[BLACK] -= e.P_KNIGHT_TRAPPED_A7; break;
         case C6: if (isPiece(BLACK, PAWN, C7) && isPiece(BLACK, PAWN, D5) && !isPiece(BLACK, PAWN, E5)) v.Blockages[BLACK] -= e.P_C3_KNIGHT; break;
         }
     }
 
     /***************************************************************
     *  Material value adjustement based on the no. of own pawns.   *
     *  Knights lose value as pawns disappear.                      *
     ***************************************************************/
 
     v.MaterialAdjustement[side] += knight_adj[b.PieceCount[side][PAWN]];
 
    /****************************************************************
    *  Collect data about mobility and king attacks. This resembles *
    *  move generation code, except that we are just incrementing   *
    *  the counters instead of adding actual moves.                 *
    ****************************************************************/
 
    for (U8 dir=0;dir<8;dir++) {
        pos = sq + vector[KNIGHT][dir];
        if ( IS_SQ(pos) && b.color[pos] != side ) {
           ++mob;
           if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] )
               ++att; // this knight is attacking zone around enemy king
        }
    }
 
    /****************************************************************
    *  Evaluate mobility. We try to do it in such a way             *
    *  that  zero represents average mobility, but  our             *
    *  formula of doing so is a puer guess.                         *
    ****************************************************************/
 
    v.mgMob[side] += 4 * (mob-4);
    v.egMob[side] += 4 * (mob-4);
 
    /****************************************************************
    *  Save data about king attacks                                 *
    ****************************************************************/
 
    if (att) {
       v.attCnt[side]++;
       v.attWeight[side] += 2 * att;
    }
}
 
void EvalBishop(S8 sq, S8 side) {
     int att = 0;
     int mob = 0;
     v.gamePhase += 1;
 
     if (side == WHITE) {
         switch (sq) {
         case A7: if (isPiece(BLACK, PAWN, B6)) v.Blockages[WHITE] -= e.P_BISHOP_TRAPPED_A7; break;
         case H7: if (isPiece(BLACK, PAWN, G6)) v.Blockages[WHITE] -= e.P_BISHOP_TRAPPED_A7; break;
         case B8: if (isPiece(BLACK, PAWN, C7)) v.Blockages[WHITE] -= e.P_BISHOP_TRAPPED_A7; break;
         case G8: if (isPiece(BLACK, PAWN, F7)) v.Blockages[WHITE] -= e.P_BISHOP_TRAPPED_A7; break;
         case A6: if (isPiece(BLACK, PAWN, B5)) v.Blockages[WHITE] -= e.P_BISHOP_TRAPPED_A6; break;
         case H6: if (isPiece(BLACK, PAWN, G5)) v.Blockages[WHITE] -= e.P_BISHOP_TRAPPED_A6; break;
         case F1: if (isPiece(WHITE, KING, G1)) v.PositionalThemes[WHITE] += e.RETURNING_BISHOP; break;
         case C1: if (isPiece(WHITE, KING, B1)) v.PositionalThemes[WHITE] += e.RETURNING_BISHOP; break;
         }
     }
     else {
         switch (sq) {
         case A2: if (isPiece(WHITE, PAWN, B3)) v.Blockages[BLACK] -= e.P_BISHOP_TRAPPED_A7; break;
         case H2: if (isPiece(WHITE, PAWN, G3)) v.Blockages[BLACK] -= e.P_BISHOP_TRAPPED_A7; break;
         case B1: if (isPiece(WHITE, PAWN, C2)) v.Blockages[BLACK] -= e.P_BISHOP_TRAPPED_A7; break;
         case G1: if (isPiece(WHITE, PAWN, F2)) v.Blockages[BLACK] -= e.P_BISHOP_TRAPPED_A7; break;
         case A3: if (isPiece(WHITE, PAWN, B4)) v.Blockages[BLACK] -= e.P_BISHOP_TRAPPED_A6; break;
         case H3: if (isPiece(WHITE, PAWN, G4)) v.Blockages[BLACK] -= e.P_BISHOP_TRAPPED_A6; break;
         case F8: if (isPiece(BLACK, KING, G8)) v.PositionalThemes[BLACK] += e.RETURNING_BISHOP; break;
         case C8: if (isPiece(BLACK, KING, B8)) v.PositionalThemes[BLACK] += e.RETURNING_BISHOP; break;
         }
     }
 
    /****************************************************************
    *  Collect data about mobility and king attacks                 *
    ****************************************************************/
 
    for (char dir=0;dir<vectors[BISHOP];dir++) {
 
         for (char pos = sq;;) {
 
              pos = pos + vector[BISHOP][dir];
              if (! IS_SQ(pos)) break;
 
              if (b.pieces[pos] == PIECE_EMPTY) {
                 mob++;
                 if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] ) ++att;
              }
              else if (b.color[pos] != side) {
                   mob++;
                   if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] ) ++att;
                   break;
              }
              else {
                   break;
              }
 
         }
    }
 
    v.mgMob[side] += 3 * (mob-7);
    v.egMob[side] += 3 * (mob-7);
 
    if (att) {
       v.attCnt[side]++;
       v.attWeight[side] += 2*att;
    }
 
 
}
 
void EvalRook(S8 sq, S8 side) {
    int att = 0;
    int mob = 0;
    int ownBlockingPawns = 0;
    int oppBlockingPawns = 0;
    int stepFwd;
    int nextSq;
 
    v.gamePhase += 2;
 
    /***************************************************************
    *  Material value adjustement based on the no. of own pawns.   *
    *  Rooks gain value as pawns disappear.                        *
    ***************************************************************/
 
    v.MaterialAdjustement[side] += rook_adj[b.PieceCount[side][PAWN]];
 
    /***************************************************************
    *  This is an ugly hack to detect open files. Merging it with  *
    *  mobility  eval would have been better, but less  readable,  *
    *  and this is educational program fter all.                   *
    /**************************************************************/
 
    if (side == WHITE) stepFwd = NORTH; else stepFwd = SOUTH;
    nextSq = sq + stepFwd;
 
    while (IS_SQ(nextSq)) {
        if (b.pieces[nextSq] == PAWN) {
            if (b.color[nextSq] == side) {
               ownBlockingPawns++;
               break;
            }
            else
               oppBlockingPawns++;
        }
        nextSq += stepFwd;
    }
 
    /****************************************************************
    *  Evaluate open and half-open files. We merge this bonus with  *
    *  mobility  score.                                             *
    /***************************************************************/
 
    if ( !ownBlockingPawns ) {
 
       if ( !oppBlockingPawns ) {
           v.mgMob[side] += e.ROOK_OPEN;
           v.egMob[side] += e.ROOK_OPEN;
       } else {
           v.mgMob[side] += e.ROOK_HALF;
           v.egMob[side] += e.ROOK_HALF;
       }
    }
 
    /****************************************************************
    *  Collect data about mobility and king attacks                 *
    ****************************************************************/
 
    for (char dir=0; dir<vectors[ROOK]; dir++) {
 
         for (char pos = sq;;) {
 
              pos = pos + vector[ROOK][dir];
              if (! IS_SQ(pos)) break;
 
              if (b.pieces[pos] == PIECE_EMPTY) {
                 mob++;
                 if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] ) ++att;
              }
              else if (b.color[pos] != side) {
                   mob++;
                   if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] ) ++att;
                   break;
              }
              else {
                   break;
              }
 
         }
    }
 
    v.mgMob[side] += 2 * (mob-7);
    v.egMob[side] += 4 * (mob-7);
 
    if (att) {
       v.attCnt[side]++;
       v.attWeight[side] += 3*att;
    }
 
}
 
void EvalQueen(S8 sq, S8 side) {
    v.gamePhase += 4;
    int att = 0;
    int mob = 0;
 
    /****************************************************************
    *  A queen should not be developed too early                    *
    ****************************************************************/
 
    if (side == WHITE && ROW(sq) > ROW_2) {
        if (isPiece(WHITE, KNIGHT, B1)) v.PositionalThemes[WHITE] -= 2;
        if (isPiece(WHITE, BISHOP, C1)) v.PositionalThemes[WHITE] -= 2;
        if (isPiece(WHITE, BISHOP, F1)) v.PositionalThemes[WHITE] -= 2;
        if (isPiece(WHITE, KNIGHT, G1)) v.PositionalThemes[WHITE] -= 2;
    }
 
    if (side == BLACK && ROW(sq) < ROW_7) {
        if (isPiece(BLACK, KNIGHT, B8)) v.PositionalThemes[BLACK] -= 2;
        if (isPiece(BLACK, BISHOP, C8)) v.PositionalThemes[BLACK] -= 2;
        if (isPiece(BLACK, BISHOP, F8)) v.PositionalThemes[BLACK] -= 2;
        if (isPiece(BLACK, KNIGHT, G8)) v.PositionalThemes[BLACK] -= 2;
    }
 
    /****************************************************************
    *  Collect data about mobility and king attacks                 *
    ****************************************************************/
 
    for (char dir=0;dir<vectors[QUEEN];dir++) {
 
         for (char pos = sq;;) {
 
              pos = pos + vector[QUEEN][dir];
              if (! IS_SQ(pos)) break;
 
              if (b.pieces[pos] == PIECE_EMPTY) {
                 mob++;
                 if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] ) ++att;
              }
              else if (b.color[pos] != side) {
                   mob++;
                   if ( e.sqNearK[!side] [b.KingLoc[!side] ] [pos] ) ++att;
                   break;
              }
              else {
                   break;
              }
 
         }
    }
 
    v.mgMob[side] += 1 * (mob-14);
    v.egMob[side] += 2 * (mob-14);
 
    if (att) {
       v.attCnt[side]++;
       v.attWeight[side] += 4*att;
    }
 
}
 
int wKingShield() {
 
    int result = 0;
 
    /* king on the kingside */
    if ( COL(b.KingLoc[WHITE]) > COL_E ) {
 
       if ( isPiece(WHITE, PAWN, F2) )  result += e.SHIELD_1;
       else if ( isPiece(WHITE, PAWN, F3) )  result += e.SHIELD_2;
 
       if ( isPiece(WHITE, PAWN, G2) )  result += e.SHIELD_1;
       else if ( isPiece(WHITE, PAWN, G3) )  result += e.SHIELD_2;
 
       if ( isPiece(WHITE, PAWN, H2) )  result += e.SHIELD_1;
       else if ( isPiece(WHITE, PAWN, H3) )  result += e.SHIELD_2;
   }
 
   /* king on the queenside */
   else if ( COL(b.KingLoc[WHITE]) < COL_D ) {
 
       if ( isPiece(WHITE, PAWN, A2) )  result += e.SHIELD_1;
       else if ( isPiece(WHITE, PAWN, A3) )  result += e.SHIELD_2;
 
       if ( isPiece(WHITE, PAWN, B2) )  result += e.SHIELD_1;
       else if ( isPiece(WHITE, PAWN, B3) )  result += e.SHIELD_2;
 
       if ( isPiece(WHITE, PAWN, C2) )  result += e.SHIELD_1;
       else if ( isPiece(WHITE, PAWN, C3) )  result += e.SHIELD_2;
   }
 
return result;
}
 
int bKingShield() {
    int result = 0;
 
  /* king on the kingside */
  if ( COL(b.KingLoc[BLACK]) > COL_E ) {
        if ( isPiece(BLACK, PAWN, F7) )  result += e.SHIELD_1;
        else if ( isPiece(BLACK, PAWN, F6) )  result += e.SHIELD_2;
 
        if ( isPiece(BLACK, PAWN, G7) )  result += e.SHIELD_1;
        else if ( isPiece(BLACK, PAWN, G6) )  result += e.SHIELD_2;
 
        if ( isPiece(BLACK, PAWN, H7) )  result += e.SHIELD_1;
        else if ( isPiece(BLACK, PAWN, H6) )  result += e.SHIELD_2;
   }
 
   /* king on the queenside */
   else if ( COL(b.KingLoc[BLACK]) < COL_D ) {
       if ( isPiece(BLACK, PAWN, A7) )  result += e.SHIELD_1;
       else if ( isPiece(BLACK, PAWN, A6) )  result += e.SHIELD_2;
 
       if ( isPiece(BLACK, PAWN, B7) )  result += e.SHIELD_1;
       else if ( isPiece(BLACK, PAWN, B6) )  result += e.SHIELD_2;
 
       if ( isPiece(BLACK, PAWN, C7) )  result += e.SHIELD_1;
       else if ( isPiece(BLACK, PAWN, C6) )  result += e.SHIELD_2;
  }
  return result;
}
 
/******************************************************************************
*                            Pawn structure evaluaton                         *
******************************************************************************/
 
int getPawnScore() {
    int result;
 
    /**************************************************************************
    *  This function wraps hashing mechanism around evalPawnStructure().      *
    *  Please note  that since we use the pawn hashtable, evalPawnStructure() *
    *  must not take into account the piece position.  In a more elaborate    *
    *  program, pawn hashtable would contain only the characteristics of pawn *
    *  structure,  and scoring them in conjunction with the piece position    *
    *  would have been done elsewhere.                                        *
    **************************************************************************/
 
    int probeval = ttpawn_probe();
    if (probeval != INVALID)
        return probeval;
 
    result = evalPawnStructure();
 
    ttpawn_save(result);
 
    return result;
}
 
int evalPawnStructure() {
    int result = 0;
 
    for (U8 row = 0; row < 8; row++)
    for (U8 col = 0; col < 8; col++) {
 
       S8 sq = SET_SQ(row, col);
 
       if (b.pieces[sq] == PAWN) {
           if (b.color[sq] == WHITE) result += EvalPawn(sq, WHITE);
           else                      result -= EvalPawn(sq, BLACK);
       }
   }
 
   return result;
}
 
int EvalPawn(S8 sq, S8 side) {
    int result = 0;
    int flagIsPassed = 1; // we will be trying to disprove that
    int flagIsWeak = 1;   // we will be trying to disprove that
    int flagIsOpposed = 0;
 
    int stepFwd, stepBck;
    if (side == WHITE) stepFwd = NORTH; else stepFwd = SOUTH;
    if (side == WHITE) stepBck = SOUTH; else stepBck = NORTH;
    S8 nextSq = sq + stepFwd;
 
    /*************************************************************************
    *   We have only very basic data structures that do not update informa-  *
    *   tion about pawns incrementally, so we have to calculate everything   *
    *   here.  The loop below detects doubled pawns, passed pawns and sets   *
    *   a flag on finding that our pawn is opposed by enemy pawn.            *
    *************************************************************************/
 
    while (IS_SQ(nextSq)) {
 
        if (b.pieces[nextSq] == PAWN) { // either opposed by enemy pawn or doubled
            flagIsPassed = 0;
            if (b.color[nextSq] == side)
                result -= 20;       // doubled pawn penalty
            else
                flagIsOpposed = 1;  // flag our pawn as opposed
        }
 
        if (IS_SQ(nextSq + WEST) && isPiece(!side, PAWN, nextSq + WEST))
            flagIsPassed = 0;
 
        if (IS_SQ(nextSq + EAST) && isPiece(!side, PAWN, nextSq + EAST))
            flagIsPassed = 0;
 
        nextSq += stepFwd;
    }
 
    /*************************************************************************
    *   Another loop, going backwards and checking whether pawn has support. *
    *   Here we can at least break out of it for speed optimization.         *
    *************************************************************************/
 
    nextSq = sq;
    while (IS_SQ(nextSq)) {
 
        if (IS_SQ(nextSq + WEST) && isPiece(side, PAWN, nextSq + WEST)) {
            flagIsWeak = 0;
            break;
        }
 
        if (IS_SQ(nextSq + EAST) && isPiece(side, PAWN, nextSq + EAST)) {
            flagIsWeak = 0;
            break;
        }
 
        nextSq += stepBck;
    }
 
    /*************************************************************************
    *  Evaluate passed pawns, scoring them higher if they are protected      *
    *  or if their advance is supported by friendly pawns                    *
    *************************************************************************/
 
    if ( flagIsPassed ) {
        if ( isPawnSupported(sq, side) ) result += e.protected_passer[side][sq];
        else                             result += e.passed_pawn[side][sq];
    }
 
    /*************************************************************************
    *  Evaluate weak pawns, increasing the penalty if they are situated      *
    *  on a half-open file                                                   *
    *************************************************************************/
 
    if ( flagIsWeak ) {
        result += e.weak_pawn[side][sq];
        if (!flagIsOpposed)
            result -= 4;
    }
 
    return result;
}
 
int isPawnSupported(S8 sq, S8 side) {
    int step;
    if (side == WHITE) step = SOUTH; else step = NORTH;
 
    if ( IS_SQ(sq+WEST) && isPiece(side,PAWN, sq + WEST) ) return 1;
    if ( IS_SQ(sq+EAST) && isPiece(side,PAWN, sq + EAST) ) return 1;
    if ( IS_SQ(sq+step+WEST) && isPiece(side,PAWN, sq + step+WEST ) ) return 1;
    if ( IS_SQ(sq+step+EAST) && isPiece(side,PAWN, sq + step+EAST ) ) return 1;
 
    return 0;
}
 
/******************************************************************************
*                             Pattern detection                               *
******************************************************************************/
 
void blockedPieces() {
 
    // central pawn blocked, bishop hard to develop
    if (isPiece(WHITE, BISHOP, C1) && isPiece(WHITE, PAWN, D2) && b.color[D3] != COLOR_EMPTY)
        v.Blockages[WHITE] -= e.P_BLOCK_CENTRAL_PAWN;
    if (isPiece(WHITE, BISHOP, F1) &&  isPiece(WHITE, PAWN, E2) && b.color[E3] != COLOR_EMPTY)
        v.Blockages[WHITE] -= e.P_BLOCK_CENTRAL_PAWN;
    if (isPiece(BLACK, BISHOP, C8) &&  isPiece(BLACK, PAWN, D7) && b.color[D6] != COLOR_EMPTY)
        v.Blockages[BLACK] -= e.P_BLOCK_CENTRAL_PAWN;
    if (isPiece(BLACK, BISHOP, F8) && isPiece(BLACK, PAWN, E7) && b.color[E6] != COLOR_EMPTY)
        v.Blockages[BLACK] -= e.P_BLOCK_CENTRAL_PAWN;
 
    // uncastled king blocking own rook
    if ( ( isPiece(WHITE, KING, F1) || isPiece(WHITE, KING, G1 ) )&&
         ( isPiece(WHITE, ROOK, H1) || isPiece(WHITE, ROOK, G1 ) )
       )
       v.Blockages[WHITE] -= e.P_KING_BLOCKS_ROOK;
 
    if ( ( isPiece(WHITE, KING, C1) || isPiece(WHITE, KING, B1 ) )&&
         ( isPiece(WHITE, ROOK, A1) || isPiece(WHITE, ROOK, B1 ) )
       )
       v.Blockages[WHITE] -= e.P_KING_BLOCKS_ROOK;
 
    if ( ( isPiece(BLACK, KING, F8) || isPiece(BLACK, KING, G8 ) )&&
         ( isPiece(BLACK, ROOK, H8) || isPiece(BLACK, ROOK, G8 ) )
       )
       v.Blockages[BLACK] -= e.P_KING_BLOCKS_ROOK;
 
    if ( ( isPiece(BLACK, KING, C8) || isPiece(BLACK, KING, B8 ) )&&
         ( isPiece(BLACK, ROOK, A8) || isPiece(BLACK, ROOK, B8 ) )
       )
       v.Blockages[BLACK] -= e.P_KING_BLOCKS_ROOK;
}
 
int isPiece(U8 color, U8 piece, S8 sq) {
    return ( (b.pieces[sq] == piece) && (b.color[sq] == color) );
}
 
/***************************************************************************************
*                             Printing eval results                                    *
***************************************************************************************/
 
void printEval() {
    printf("------------------------------------------\n");
    printf("Total value (for side to move): %d \n", eval(-INF,INF, 0) );
    printf("Material balance    : %d \n", b.PieceMaterial[WHITE] + b.PawnMaterial[WHITE] - b.PieceMaterial[BLACK] - b.PawnMaterial[BLACK] );
    printf("Material adjustement: "); printEvalFactor(v.MaterialAdjustement[WHITE], v.MaterialAdjustement[BLACK]);
    printf("Mg Piece/square tables : "); printEvalFactor(b.PcsqMg[WHITE], b.PcsqMg[BLACK]);
    printf("Eg Piece/square tables : "); printEvalFactor(b.PcsqEg[WHITE], b.PcsqEg[BLACK]);
    printf("Mg Mobility            : "); printEvalFactor(v.mgMob[WHITE], v.mgMob[BLACK]);
    printf("Eg Mobility            : "); printEvalFactor(v.mgMob[WHITE], v.egMob[BLACK]);
    printf("Pawn structure      : %d \n", evalPawnStructure() );
    printf("Blockages           : "); printEvalFactor(v.Blockages[WHITE], v.Blockages[BLACK]);
    printf("Positional themes   : "); printEvalFactor(v.PositionalThemes[WHITE], v.PositionalThemes[BLACK]);
    printf("King Shield         : "); printEvalFactor(v.kingShield[WHITE], v.kingShield[BLACK]);
    printf("Tempo: ");
    if ( b.stm == WHITE ) printf("%d", e.TEMPO); else printf("%d", -e.TEMPO);
    printf("\n");
    printf("------------------------------------------\n");
}
 
void printEvalFactor(int wh, int bl) {
     printf("white %4d, black %4d, total: %4d \n", wh, bl, wh - bl);
}

What links here?

Page	Date Edited
CPW-Engine	Dec 31, 2014
CPW-Engine_eval	Jul 26, 2016
Evaluation	Feb 1, 2018
Evaluation function	Sep 24, 2010
King Safety	Feb 14, 2018
Lazy Evaluation	Sep 1, 2017
Mobility	Jan 17, 2018
Pawn Structure	Oct 2, 2017

Up one Level

CPW-Engine_eval_h

Pawel_Koziol — Tue, 30 Dec 2014 18:56:27 GMT

Home * Engines * CPW-Engine * eval.h

eval.h

 
/* king safety*/
int wKingShield();
int bKingShield();
 
/* pawn structure */
int getPawnScore();
int evalPawnStructure();
int EvalPawn(S8 sq, S8 side);
void EvalKnight(S8 sq, S8 side);
void EvalBishop(S8 sq, S8 side);
void EvalRook(S8 sq, S8 side);
void EvalQueen(S8 sq, S8 side);
int isPawnSupported(S8 sq, S8 side);
int isWPSupported(S8 sq);
int isBPSupported(S8 sq);
 
/* pattern detection */
void blockedPieces();

Up one Level

Development

GerdIsenberg — Wed, 17 Jan 2018 18:58:47 GMT

Home * Evaluation * Game Phases * Opening * Development

Development,
the aim of the opening phase to move pieces from their initial to more advantageous squares to occupy or control the center to gain more space and more attack and defense possibilities, or activity, as well in hindering the opponent from doing so. In the first place this applies to the minor pieces, knights and bishops, in conjunction with the required pawn moves to open lines aka diagonals to mobilize bishops.

Tempo Balance

Each development of a minor piece, and finally connecting the rooks on the base rank due to castling, is considered a tempo, but no pawn move. In tempo sensitive, often dynamic and tactical opening lines with early pawn levers and options to open attacking lines, Tarrasch rates three tempi worth about the value of one pawn ^[1], which is the motivation of many gambit openings.

A rule of thumb is not only to avoid premature pawn moves, but also moving the same piece multiple times without a good reason, and to develop most or at best all minor pieces before starting attacks with the queen - since too early queen development without appropriate support makes her vulnerable to opponent piece attacks, often worsening the tempo balance if not worse.

Eval Considerations

While some programs rely on the opening book and appropriate piece-square tables as well as mobility and center control terms to encourage piece development, others have explicit terms for this early game phase and consider a tempo balance and extra penalties for knights and bishops on their initial home squares. Quite common is also to punish blocked center pawns on their base rank, and too early development of the queen.

Forum Posts

2 Dh5? by Henk van den Belt, CCC, July 13, 2013

External Links

Chess

Misc

References

^ Siegbert Tarrasch (1931). Das Schachspiel Die Eröffnung I. Allgemeiner Teil Die Kräfte, Der Raum, Die Zeit, Tempobilanz (German)

What links here?

Page	Date Edited
Belzebub	Oct 20, 2015
Center Control	Jan 17, 2018
Development	Jan 17, 2018
Diablo	May 17, 2016
Djinn	Feb 8, 2016
Dreamer	Aug 5, 2015
Exacto	Oct 3, 2015
Fianchetto	Feb 18, 2015
Game Phases	May 22, 2017
Gibbon	Dec 23, 2014
Gk	Oct 9, 2017
HAL	Jun 14, 2016
MChess	Feb 26, 2018
Neurosis	Dec 23, 2017
Opening	Jun 25, 2017
Ostrich	Dec 28, 2017
Paul W. Abrahams	Oct 9, 2013
Prophet	Sep 30, 2017
RattateChess	Dec 2, 2014
RDChess	May 3, 2013
Returning Bishop	Mar 16, 2013
Sinobyl	Jul 3, 2014
Space	Jan 18, 2018
Spector	Nov 11, 2016
Sunsetter	Jun 1, 2017
Tempo	Jan 17, 2018
Wing	Oct 26, 2017

Up one level

Doubled Pawn

GerdIsenberg — Mon, 27 Mar 2017 14:06:49 GMT

Types of Doubled Pawns

Crippled Majority

All About Doubled Pawns

Home * Evaluation * Pawn Structure * Doubled Pawn

A Pawn is doubled (or, sad to say, tripled) if there are more pawns of the same color on a given file. Early programs and papers advocated evaluation it as half a pawn, today the penalty uses to be less severe.

It might be argued that, given a perfect pawn structure evaluation, a term such as doubled pawns would not be needed, since they usually introduce another pawn structure weaknesses: some kind of backwardness, lack of a candidate passer that otherwise would be there etc. However, most evaluation functions do not go into such details as to justify removing this term.

Types of Doubled Pawns

Hans Berliner characterizes doubled pawns by their exchange potential against opponent pawns on adjacent files ^[1]^[2]:

		`The doubled pawns on the b-file are the best situation, the f-file pawns are next. The h-file pawns are the worst situation because two pawns are held back by one opposing pawn, so the second pawn has little value.`
8/1p6/p3pp1p/8/8/1PP2P1P/1P3P1P/8 w - -

Crippled Majority

A doubled pawn of a so called "crippled" majority devalue that majority. Hans Berliner on following position in Some Innovations Introduced by Hitech ^[3]:

		`Our pawn-structure algorithm is quite simple, detecting only isolated and multiple Pawns, and the effect of multiple Pawns on viable pawn majorities. For instance, in Diagram 1 the value of White's doubled Pawn is negligible, while Black's is almost full-valued. This distinction, an innovation, was first introduced in Patsoc ^[4], and we were able to adapt the code to make up tables to be loaded into six identical hardware units computing pawn structure in parallel.`
8/1pp2ppp/1p6/8/8/5P2/PPP2PPP/8 w - -

All About Doubled Pawns

The vast amount of detailed knowledge about doubled pawns, which rarely gets implemented, can be found in the article All About Doubled Pawns by Larry Kaufman ^[5].

Publications

Larry Kaufman (2005). All About Doubled Pawns. (first published in Chess Life, May 2005, p.22)

Forum Posts

Doubled and Backward Pawn Engine "Definitions" by Brian Richardson, CCC, September 07, 2009
passed but doubled pawns by Jon Dart, CCC, June 15, 2015 » Passed Pawn
Doubled pawns by Stefano Gemma, CCC, May 11, 2016

External Links

Doubled Pawn from Wikipedia
All About Doubled Pawns by IM Larry Kaufman
Doubled Pawns Strength and weakness by Georgi Orlov
Ástor Piazzolla - Tristezas de un Doble A, Montreux Jazz Festival, 1985 YouTube Videos

References

^ Hans Berliner (1999). The System: A World Champion's Approach to Chess, Gambit Publications, ISBN 1-901983-10-2
^ Types of doubled pawns from Wikipedia
^ Hans Berliner (1987). Some Innovations Introduced by Hitech. ICCA Journal, Vol. 10, No. 3, pp. 111-117
^ Hans Berliner (1985). Computer Chess at Carnegie Mellon University. Advances in Computer Chess 4
^ Larry Kaufman (2005). All About Doubled Pawns. (first published in Chess Life, May 2005, p.22)

What links here?

Page	Date Edited
More Links
Alarm	Jun 7, 2017
Amyan	Dec 10, 2013
Applied Concepts	Dec 25, 2017
Bismark	Apr 22, 2015
Cheese	Feb 17, 2018
Cheops (Miller)	Jan 13, 2017
chess22k	Apr 23, 2018
ChessBrainVB	Jun 24, 2017
Clarabit	Nov 3, 2011
Coiled	Mar 12, 2017
Cyrus	Jul 22, 2014
Dartmouth CP	Jan 19, 2018
Defenchess	Mar 18, 2018
Diablo	May 17, 2016
Dispersion and Distortion	Nov 11, 2017
Donna	Aug 17, 2017
Dorpsgek	Apr 2, 2018
Double and Triple (Bitboards)	May 12, 2016
Doubled Pawn	Mar 27, 2017
Eval Tuning in Deep Thought	Jun 7, 2016
Evaluation function draft	Jul 11, 2014
Evaluation Overlap	Jul 19, 2016
Exacto	Oct 3, 2015
Garbochess	Sep 23, 2017
Gk	Oct 9, 2017
Gogobello	Jul 30, 2017
GopherCheck	Feb 3, 2017
Hakkapeliitta	Apr 26, 2016
Hans Kmoch	Nov 11, 2017
ISA	Apr 2, 2018
Kaissa	Apr 9, 2018
Larry Kaufman	Jan 28, 2018
Marvin	Feb 10, 2018
Nebula	Feb 7, 2015
Neurosis	Dec 23, 2017
Passed Pawn	Oct 11, 2017
Patsoc	Jan 7, 2016
Paul	Jan 8, 2016
Pawn Center	Feb 12, 2015
Pawn Majority	Jun 8, 2012

Up one Level

Draw Evaluation

GerdIsenberg — Mon, 12 Mar 2018 19:06:54 GMT

Home * Evaluation * Game Phases * Endgame * Draw Evaluation

It is important for the evaluation function to recognize the endgame positions which are likely drawn, even though one side has a nominal advantage. This page is devoted to listing some of them. More of that kind of knowledge can be found in the KPK section.

Obvious Draws

According to the FIDE rules, the position is an immediate draw if

both sides have a bare king
one side has a king and a minor piece against a bare king
one side has two knights against the bare king
both sides have a king and a bishop, the bishops being the same color

Simple Heuristics

The bishops of different colors are not counted as an immediate draw, because of the possibility of a helpmate in the corner. Since this is unlikely given even a four ply search, we may introduce another class of drawn positions: those that cannot be claimed, but can be evaluated as draws:

both sides have a king and a minor piece each
the weaker side has a minor piece against two knights
two bishops draw against a bishop
two minor pieces against one draw, except when the stronger side has a bishop pair

Please note that a knight or even two knights against two bishops are not included here, as it is possible to win this ending.

Implementation note: When a program uses heuristics of that kind, it is of utmost importance to be consistent. For example, if KBN vs KB is scored as a draw, the same must be done with KBN vs KBP. A possible idea is to divide a score by a large constant, such as 16 or 32, when the side nominally ahead has the wrong piece combination.

Complex Heuristics

If the stronger side has a pawn and a bishop against the minor piece, the position is almost certainly drawn if the weaker side's king occupies a square on the path of a pawn that is inaccessible to the enemy bishop (the degenerate cases like trapping the minor piece are best left for search).
KQKP with rook pawn or bishop pawn on its seventh rank supported by own king threatening stalemate, other king far enough away ^[1] ^[2]
Wrong color Bishop and Rook Pawn

One-sided Heuristics

There are some heuristics that tells us only that one of the players cannot win the endgame

a single minor piece should not win against any number of pawns (again, there is a special case of a smothered mate in the corner, but it is search issue, not to be taken into account by the evaluation function)

Forum Posts

2000 ...

"Don't trust draw score" <=Is it true? by Teerapong Tovirat, CCC, August 08, 2001 » Repetitions, Transposition Table, Path-Dependency
Draw recognition by eval problems by Rafael Andrist, CCC, October 17, 2001 » Wrong color Bishop and rook pawn

2010 ...

question on draw evaluation by liuzy, CCC, June 03, 2010
Re: question on draw evaluation by Robert Houdart, CCC, June 07, 2010
handling draw by insufficient material by Youri Matiounine, November 19, 2013 » Insufficient Material
evaluating tablebases draws by Uri Blass, CCC, November 23, 2013 » Endgame Tablebases
Discocheck 5.01: Bishop related endgame problems by Mike Scheidl, CCC, November 25, 2013 » DiscoCheck, Color of a Square, Wrong color Bishop and Rook Pawn

2015 ...

Stockfish eval output by Erin Dame, CCC, August 27, 2016 » Wrong color Bishop and rook pawn, Stockfish
draw endgame scaling by Alexandru Mosoi, CCC, December 04, 2016
insufficient mating material by Erin Dame, CCC, May 29, 2017
Marcel Duchamp endgame "splits" engines / hash phenomenon by Kenneth Regan, CCC, February 19, 2018 » Chess Problems, Compositions and Studies, Marcel Duchamp, Transposition Table
what to do when all depths give the exact same score? by Sander Maassen vd Brink, CCC, March 10, 2018

References

^ Marcel Duchamp endgame "splits" engines / hash phenomenon by Kenneth Regan, CCC, February 19, 2018 » Chess Problems, Compositions and Studies, Marcel Duchamp, Transposition Table
^ what to do when all depths give the exact same score? by Sander Maassen vd Brink, CCC, March 10, 2018

What links here?

Page	Date Edited
Alexandru Mosoi	Jan 8, 2018
Blockage Detection	Oct 19, 2017
Contempt Factor	Mar 10, 2018
Draw	Apr 14, 2018
Draw Evaluation	Mar 12, 2018
Endgame	Sep 18, 2017
Endgame Tablebases	Mar 6, 2018
Erin Dame	Dec 2, 2017
Fortress	Feb 1, 2018
Gibbon	Dec 23, 2014
Gogobello	Jul 30, 2017
Houdini	Apr 14, 2018
Interior Node Recognizer	Mar 12, 2018
KingSlayer	Oct 17, 2016
Material	Mar 31, 2018
Murka	Nov 11, 2016
python-chess	Nov 4, 2017
Robert Houdart	Dec 8, 2017
Score	Apr 14, 2018
Stalemate	Feb 21, 2018
Terminal Node	Nov 17, 2012
Vice	Mar 8, 2016
Youri Matiounine	Jul 20, 2015

Up one Level

Endgame

GerdIsenberg — Mon, 18 Sep 2017 18:50:17 GMT

Home * Evaluation * Game Phases * Endgame

		In the Endgame chess programs usually have quite a lot of difficulties. Even the most simple endgames often just lead to a mate after 10 to 15 plys or more, which is far beyond the horizon for engines without the specific endgame knowledge. There are some concepts that a chess programmer should implement to overcome the most basic problems. Usually chess engines activate this special Endgame knowledge as soon as the material on board reaches a certain lower-bound.
Dorothea Tanning - End Game, 1944 ^[1]

Transposition Tables

Nowhere else are the Transposition Hash Tables more efficient than in Endgames. They are invaluable.

Evaluation

When doing a positional evaluation, in the endgame, the chess engines should change some parameters. So for example in the middlegame, if the opponent's king is trapped in the center, it should be evaluated much better for the program than the opponent's king, safely standing at the border. In the endgame however, its the other way round. A king on the edge or even in the corner has not so many squares to escape to and is more beneficial for the other player. Furthermore, if you only have one bishop, it might be good to evaluate the opponent's king being forced to go to the corner with the color of the bishop a higher bonus, than for the other corner. In addition Pawn promotion is a very important aim in most endgames. The chess engines should consider that by evaluating the strength of passed pawns.

Special Knowledge

Some endgames are extensively covered by theory, and for that reason one can supply a vast number of heuristics for playing them. Typical examples include:

King and Pawns

Pieces

Endgame Tablebases

see main article Endgame Tablebases.

Currently many engines support the usage of Endgame Tablebases, precalculated databases that hold for every possible position in a certain endgame, whether it is drawn or how many moves are left for a side to win/lose assuming perfect play. With the help of those, chess engines can simply lookup, if it is advisable to do a certain exchange or not, as well as how to finish some of the more tricky endgames. The advantage of Endgame Tablebases is the ability to determine the definite outcome of a certain position, but on the other side, Tablebases are very space consuming and the disk-access tends to be slow.

Publications

1960 ...

Barbara J. Huberman (1968). A Program to Play Chess End Games. Technical Report no. CS-106, Ph.D. thesis. Stanford University, Computer Science Department

1970 ...

Coen Zuidema (1974). Chess: How to Program the Exceptions? Technical Report IW21/74, Department of Informatics, Mathematical Center Amdsterdam. pdf
Pericles Negri (1977). Inductive Learning in a Hierarchical Model for Representing Knowledge in Chess End Games. pdf
Ryszard Michalski, Pericles Negri (1977). An experiment on inductive learning in chess endgames. Machine Intelligence 8, pdf
Kenneth W. Church (1978). Co-ordinate Squares: A Solution to Many Chess Pawn Endgames. B.Sc. thesis, Massachusetts Institute of Technology, reprinted 1988 in Computer Chess Compendium » Corresponding Squares
Ivan Bratko, Tim Niblett (1979). Conjectures and Refutations in a Framework for Chess Endgames. in Expert Systems in the Micro-Electronic Age (Donald Michie, ed.), Edinburgh: Edinburgh University Press, 1979.
Ross Quinlan (1979). Discovering Rules by Induction from Large Collections of Examples. Expert Systems in the Micro-electronic Age, pp. 168-201. Edinburgh University Press (Introducing ID3)

1980 ...

Danny Kopec, Tim Niblett (1980). How Hard is the Play of the King-Rook-King-Knight Ending? Advances in Computer Chess 2
John F. White (1981). Chess-End-Game. Your Computer, December 1981
Alen Shapiro, Tim Niblett (1982). Automatic Induction of Classification Rules for Chess End game. Advances in Computer Chess 3
Ross Quinlan (1983). Learning efficient classification procedures and their application to chess end games. In Machine Learning: An Artificial Intelligence Approach, pages 463–482. Tioga, Palo Alto
Denis Verhoef, Jacco H. Wesselius (1987). Two-ply KRKN: Safely overtaking Quinlan. ICCA Journal, Vol. 10, No. 4
Lars Rasmussen (1987). Correcting grandmasters' Analyses in Elementary Endgames. ICCA Journal, Vol. 10, No. 4

1990 ...

Lars Rasmussen (1992). Queen versus Rook and Pawn. ICCA Journal, Vol. 15, No. 2
Wilhelm Barth, Stephan Barth (1992). Validating a Range of Endgame Programs. ICCA Journal, Vol. 15, No. 3
Jean-Christophe Weill (1994). How Hard is the Correct Coding of an Easy Endgame? Advances in Computer Chess 7, zipped ps

1995 ...

Lewis Stiller (1996). Multilinear Algebra and Chess Endgames. Games of No Chance edited by Richard J. Nowakowski, pdf
Roberto Cifuentes, Maarten De Zeeuw, Jan van Reek (1997). Secrets of Chess Endings. ICCA Journal, Vol. 20, No. 4
Kevin Coplan (1998). Synthesis of Chess and Chess-like Endgames by Recursive Optimisation. ICCA Journal, Vol. 21, No. 3
Noam D. Elkies (1999). On numbers and endgames: Combinatorial game theory in chess endgames. pdf, differs only in trivial stylistic details from the (1996) version published in Games of No Chance edited by Richard J. Nowakowski
Philip G. K. Reiser, Patricia J. Riddle (1999). Evolving Logic Programs to Classify Chess-Endgame Positions. Simulated Evolution and Learning, Canberra, Australia. Lecture Notes in Artificial Intelligence, No. 1585, Springer, pdf » Learning

2000 ...

Matthias Lüscher (2000). Automatic Generation of an Evaluation Function for Chess Endgames. ETH Zurich Supervisors: Thomas Lincke and Christoph Wirth, pdf » Neural Networks
Kevin Coplan (2001). Synthesis of Chess and Chess-like Endgames: A Proof of Correctness. ICCA Journal, Vol. 24, No. 1
Kevin Coplan (2001). Synthesis of Chess-like Endgames: Towards a Proof of Correctness. Advances in Computer Games 9

2005 ...

Ami Hauptman, Moshe Sipper (2005). GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players. EuroGP 2005, pdf
Omid David, Ariel Felner, Nathan S. Netanyahu (2006). Blockage Detection in Pawn Endgames. LNCS 3846, Springer
Ami Hauptman, Moshe Sipper (2007). Emergence of Complex Strategies in the Evolution of Chess Endgame Players. Advances in Complex Systems 10
John Nunn (2009). Understanding Chess Endgames. Gambit ^[2]

2010 ...

John Nunn (2010). Nunn's Chess Endings, volume 1. Gambit ^[3]
John Nunn (2010). Nunn's Chess Endings, volume 2. Gambit
John Nunn (2013). Discoveries in R+2P vs. R+P Endings. ICGA Journal, Vol. 36, No. 3 » Lomonosov Tablebases
Karsten Müller, Guy Haworth (2013). Rook vs. Bishop. ICGA Journal, Vol. 36, No. 4
Matej Guid, Martin Možina, Ciril Bohak, Aleksander Sadikov, Ivan Bratko (2013). Building an Intelligent Tutoring System for Chess Endgames. CSEDU 2013

2015 ...

Michael Hartisch, Ingo Althöfer (2015). Optimal Robot Play in Certain Chess Endgame Situations. ICGA Journal, Vol. 38, No. 3
Efstratios Grivas (2017). The Modern Endgame Manual. ICGA Journal, Vol. 39, No. 2
Karsten Müller (2017). Tablebases, Fermat, Knights and Knightmares. ICGA Journal, Vol. 39, No. 2
Guy Haworth (2017). Understanding Rook Endgames. ICGA Journal, Vol. 39, No. 2

Forum Posts

2000 ...

Endgame evaluation by Benny Antonsson, CCC, November 25, 2001
Piece Values in the Endgame by Guy Haworth, CCC, February 11, 2004 » Point Value
transition to endgame by Eric Oldre, CCC, April 02, 2004
Improving the endgame of my engine by Albert Bertilsson, CCC, April 08, 2004 » Sharper
Definition of "endgame"? by Jouni Uski, CCC, January 30, 2008

2010 ...

End-game evaluation by H.G.Muller, CCC, October 04, 2011
Start EndGame - A matter of fine tuning ??? by Lourenço Araujo de Oliveira Junior, CCC, February 09, 2013
End Game Heuristics by Laurie Tunnicliffe, CCC, March 20, 2014
Define end game by Laurie Tunnicliffe, CCC, November 16, 2016 » Tapered Eval
Testing endgame strength by Álvaro Begué, CCC, June 21, 2017 » Engine Testing, RuyDos

External Links

References

^ Dorothea Tanning, End Game, 1944, from artnet Magazine - We Are Duchampians by Ben Davis
^ Nunn on Understanding Chess Endgames, ChessBase News, September 13, 2009
^ Nunn’s Chess Endings Volume One, ChessBase News, May 28, 2010

What links here?

Page	Date Edited
Advances in Computer Chess 4	Jan 22, 2018
AI Chess	Jan 7, 2016
Albert Bertilsson	Jun 6, 2017
Alen Shapiro	May 3, 2015
Alexs	Dec 7, 2017
Andscacs	Jan 16, 2018
Attack and Defend Maps	Nov 5, 2016
Bishops of Opposite Colors	Aug 22, 2012
Bismark	Apr 22, 2015
Bison	Sep 29, 2016
Blockade	Oct 19, 2017
Blockage Detection	Oct 19, 2017
Capture (program)	Jan 8, 2016
Center Distance	May 24, 2015
Cheese	Feb 17, 2018
CHEKMO-II	Jan 3, 2018
Chess Problems, Compositions and Studies	Feb 19, 2018
chess22k	Apr 23, 2018
ChessLin	May 17, 2017
Clarabit	Nov 3, 2011
Coen Zuidema	Mar 25, 2016
Coiled	Mar 12, 2017
Corresponding Squares	Oct 9, 2017
Critter	Jan 25, 2014
Cupcake	May 19, 2016
Danny Kopec	Oct 1, 2016
Dartmouth CP	Jan 19, 2018
DayDreamer	Aug 22, 2016
Deep Learning	Feb 12, 2018
Delta Pruning	Dec 2, 2014
Denis Verhoef	May 21, 2013
Diablo	May 17, 2016
Diagonal Mirroring	Jun 29, 2013
Dietmar Lippold	Mar 22, 2014
Dinesh Gadwal	May 23, 2016
Distance	Sep 25, 2015
Djinn	Feb 8, 2016
Donald Michie	Dec 23, 2017
Donna	Aug 17, 2017
Double Null Move	May 8, 2015
Draw	Apr 14, 2018
Draw Evaluation	Mar 12, 2018
Eigenmann Endgame Test	Jun 1, 2017
Endgame	Sep 18, 2017
Endgame Tablebases	Mar 6, 2018
Engine Testing	Feb 1, 2018
Eval Tuning in Deep Thought	Jun 7, 2016
Evaluation	Feb 1, 2018
Evaluation function	Sep 24, 2010
Evaluation function draft	Jul 11, 2014
Evaluation of Pieces	Jan 8, 2018
Eveann	Apr 3, 2017
Falcon	Sep 2, 2016
Fifty-move Rule	Aug 9, 2017
Fischerle	Aug 21, 2017
Fortress	Feb 1, 2018
Game Phases	May 22, 2017
Genetic Programming	Dec 26, 2017
Genie	Feb 7, 2016
Gibbon	Dec 23, 2014
GullChess	Jun 27, 2017
Guy Haworth	Nov 20, 2017
Hannibal	Oct 4, 2017
Hans Zellner	Jun 12, 2016
Harm Geert Muller	Mar 31, 2018
Hedgehog	Feb 18, 2018
Huberman	Aug 9, 2013
ISA	Apr 2, 2018
Ivan Bratko	Aug 16, 2017
J. Biit	Jan 17, 2017
Jacco H. Wesselius	May 23, 2016
John F. White	Dec 11, 2013
Jouni Uski	Sep 28, 2017
Karsten Müller	Oct 19, 2017
KBNK Endgame	Nov 26, 2016
King Centralization	Oct 30, 2017
King Pawn Tropism	Sep 1, 2014
Komodo	Mar 6, 2018
KPK	May 12, 2016
KRK	Nov 26, 2016
LarsenVB	Jun 24, 2017
Laurie Tunnicliffe	Jun 13, 2017
Learning	Feb 20, 2018
Lewis Stiller	Oct 11, 2016
Linked List	Oct 11, 2016
Ludwig Zagler	Sep 6, 2017
Maarten van Emden	May 23, 2016
MadChess	Jul 4, 2017
Manhattan-Distance	May 19, 2014
Matant	Aug 14, 2013
Max Bramer	May 20, 2014
MChess	Feb 26, 2018
Meel Velliste	Sep 5, 2015
Mephisto Roma	Sep 1, 2017
Middlegame	Sep 6, 2016
Mobility	Jan 17, 2018
Module	Sep 1, 2017
Mop-up Evaluation	Nov 26, 2016
Morphy	Dec 25, 2017
Much	Jun 7, 2016
Myopic	Nov 11, 2016
Nemorino	Dec 21, 2017
Neural Networks	Mar 12, 2018
Nexus	Jan 7, 2016
NoraGrace	Nov 23, 2014
Null Move Pruning	Dec 2, 2017
Nullmover	Jul 21, 2013
Numpty chess	Oct 21, 2014
Opening	Jun 25, 2017
Opposition	Dec 2, 2014
Oracle	Sep 4, 2016
Orion	Oct 15, 2017
Outside passed pawn	Jun 7, 2012
Paradise	Nov 19, 2014
Patzer	Apr 5, 2017
Pawn Endgame	Oct 11, 2017
Pericles Negri	Dec 21, 2011
Phalanx	May 2, 2016
Phoenix (Rahul)	Mar 31, 2016
Piece-Square Tables	Mar 31, 2018
Playing Strength	Mar 31, 2018
Point Value	Mar 31, 2018
ProbCut	Mar 25, 2016
ProChess IT	Sep 30, 2015
Purple Haze	Oct 26, 2017
RedQueen	Nov 13, 2017
Rook Endgame	Sep 18, 2017
Ross Quinlan	May 23, 2016
Rule of the Square	Sep 6, 2014
Scidlet	May 31, 2014
Sharper	Jun 6, 2017
Shess	Jan 7, 2016
Sierżant	Apr 15, 2014
Sinobyl	Jul 3, 2014
Soei Tan	Oct 27, 2012
Soldat	Aug 20, 2017
SOS	Apr 5, 2017
Spector	Nov 11, 2016
Stalemate	Feb 21, 2018
Steinitz	Nov 10, 2014
Stephan Barth	May 20, 2012
Stephen Muggleton	May 23, 2016
Stockfish	Apr 7, 2018
SugaR	Oct 9, 2017
SuperChess	Dec 7, 2016
Superpawn	Mar 9, 2015
Tactics	Jan 12, 2018
Tapered Eval	Jan 9, 2018
Tarrasch Rule	Jun 2, 2014
Tempo	Jan 17, 2018
The Baron	Jan 8, 2018
The King	Jan 21, 2018
Thomas Ströhlein	May 24, 2016
Thor's Hammer	Nov 23, 2013
Tim Niblett	May 22, 2014
Triangulation	Apr 4, 2013
Tucano	Dec 16, 2017
Tunguska	Sep 16, 2017
Turochamp	Dec 14, 2017
Ulrich Thiemonds	Apr 26, 2014
Vertical Flipping	Oct 10, 2013
Wasp	Nov 24, 2017
Wilhelm	Oct 6, 2015
Wing	Oct 26, 2017
Winglet	Apr 26, 2015
Wrong color Bishop and rook pawn	Oct 19, 2017
Xpdnt	Sep 13, 2015
Yace	Dec 31, 2017
Your Computer	Dec 10, 2016
Zugzwang	Dec 8, 2017
Zurichess	Mar 12, 2018

Up one level

Evaluation

GerdIsenberg — Thu, 01 Feb 2018 19:24:36 GMT

Home * Evaluation

		An evaluation function is used to heuristically determine the relative value of a position, i.e. the chances of winning. If we could see to the end of the game in every line, the evaluation would only have values of -1 (loss), 0 (draw), and 1 (win). In practice, however, we do not know the exact value of a position, so we must make an approximation. Beginning chess players learn to do this starting with the value of the pieces themselves. Computer evaluation functions also use the value of the material as the most significant aspect and then add other considerations.
Wassily Kandinsky, Schach-Theorie, 1937 ^[1]

Where to Start

The first thing to consider when writing an evaluation function is how to score a move in Minimax or the more common NegaMax framework. While Minimax usually associates the white side with the max-player and black with the min-player and always evaluates from the white point of view, NegaMax requires a symmetric evaluation in relation to the side to move. We can see that one must not score the move per se – but the result of the move (i.e. a positional evaluation of the board as a result of the move). Such a symmetric evaluation function was first formulated by Claude Shannon in 1949 ^[2] :

f(p) = 200(K-K')
       + 9(Q-Q')
       + 5(R-R')
       + 3(B-B' + N-N')
       + 1(P-P')
       - 0.5(D-D' + S-S' + I-I')
       + 0.1(M-M') + ...
 
KQRBNP = number of kings, queens, rooks, bishops, knights and pawns
D,S,I = doubled, blocked and isolated pawns
M = Mobility (the number of legal moves)

Here, we can see that the score is returned as a result of subtracting the current side's score from the equivalent evaluation of the opponent's board scores (indicated by the prime letters K' Q' and R'.. ).

Side to move relative

In order for NegaMax to work, it is important to return the score relative to the side being evaluated. For example, consider a simple evaluation, which considers only material and mobility:

materialScore = kingWt  * (wK-bK)
              + queenWt * (wQ-bQ)
              + rookWt  * (wR-bR)
              + knightWt* (wN-bN)
              + bishopWt* (wB-bB)
              + pawnWt  * (wP-bP)
 
mobilityScore = mobilityWt * (wMobility-bMobility)

return the score relative to the side to move (who2Move = +1 for white, -1 for black):

Eval  = (materialScore + mobilityScore) * who2Move

Linear vs. Nonlinear

Most evaluations terms are a linear combination of independent features and associated weights in the form of

$\displaystyle Eval = \sum_{i=1}^{n} {Fi * Wi}$

A function f is linear if the function is additive:

$f(a+b) = f(a) + f(b)$

and second if the function is homogeneous of degree 1:

$f(c*a) = c * f(a)$

It depends on the definition and independence of features and the acceptance of the axiom of choice (Ernst Zermelo 1904), whether additive real number functions are linear or not ^[3] . Features are either related to single pieces (material), their location (piece-square tables), or more sophisticated, considering interactions of multiple pawns and pieces, based on certain patterns or chunks. Often several phases to first process simple features and after building appropriate data structures, in consecutive phases more complex features based on patterns and chunks are used.

Based on that, to distinguish first-order, second-order, etc. terms, makes more sense than using the arbitrary terms linear vs. nonlinear evaluation ^[4] . With respect to tuning, one has to take care that features are independent, which is not always that simple. Hidden dependencies may otherwise make the evaluation function hard to maintain with undesirable nonlinear effects.

General Aspects

Basic Evaluation Features

Considering Game Phase

Game Phases
Opening
Middlegame
Endgame
Evaluation Discontinuity
Tapered Eval (a score is interpolated between opening and endgame based on game stage/pieces)

Miscellaneous

Publications

1949

Claude Shannon (1949). Programming a Computer for Playing Chess. pdf from The Computer History Museum

1950 ...

Eliot Slater (1950). Statistics for the Chess Computer and the Factor of Mobility, Proceedings of the Symposium on Information Theory, London. Reprinted 1988 in Computer Chess Compendium, pp. 113-117. Including the transcript of a discussion with Alan Turing and Jack Good
Alan Turing (1953). Chess. part of the collection Digital Computers Applied to Games, in Bertram Vivian Bowden (editor), Faster Than Thought, a symposium on digital computing machines, reprinted 1988 in Computer Chess Compendium, reprinted 2004 in The Essential Turing, google books

1960 ...

Israel Albert Horowitz, Geoffrey Mott-Smith (1960,1970,2012). Point Count Chess. Samuel Reshevsky (Introduction), Sam Sloan (2012 Introduction), Amazon ^[5]
Jack Good (1968). A Five-Year Plan for Automatic Chess. Machine Intelligence II pp. 110-115

1970 ...

Ron Atkin (1972). Multi-Dimensional Structure in the Game of Chess. In International Journal of Man-Machine Studies, Vol. 4
Ron Atkin, Ian H. Witten (1975). A Multi-Dimensional Approach to Positional Chess. International Journal of Man-Machine Studies, Vol. 7, No. 6
Gerard Zieliński (1976). Simple Evaluation Function. Kybernetes, Vol. 5, No. 3
Ron Atkin (1977). Positional Play in Chess by Computer. Advances in Computer Chess 1
David Slate, Larry Atkin (1977). CHESS 4.5 - The Northwestern University Chess Program. Chess Skill in Man and Machine (ed. Peter W. Frey), pp. 82-118. Springer-Verlag, New York, N.Y. 2nd ed. 1983. ISBN 0-387-90815-3. Reprinted (1988) in Computer Chess Compendium
Hans Berliner (1979). On the Construction of Evaluation Functions for Large Domains. IJCAI 1979 Tokyo, Vol. 1, pp. 53-55.

1980 ...

Helmut Horacek (1984). Some Conceptual Defects of Evaluation Functions. ECAI-84, Pisa, Elsevier
Peter W. Frey (1985). An Empirical Technique for Developing Evaluation Functions. ICCA Journal, Vol. 8, No. 1
Tony Marsland (1985). Evaluation-Function Factors. ICCA Journal, Vol. 8, No. 2, pdf
Jens Christensen, Richard Korf (1986). A Unified Theory of Heuristic Evaluation functions and Its Applications to Learning. Proceedings of the AAAI-86, pp. 148-152, pdf
Dap Hartmann (1987). How to Extract Relevant Knowledge from Grandmaster Games. Part 1: Grandmasters have Insights - the Problem is what to Incorporate into Practical Problems. ICCA Journal, Vol. 10, No. 1
Dap Hartmann (1987). How to Extract Relevant Knowledge from Grandmaster Games. Part 2: the Notion of Mobility, and the Work of De Groot and Slater. ICCA Journal, Vol. 10, No. 2
Bruce Abramson, Richard Korf (1987). A Model of Two-Player Evaluation Functions. AAAI-87. pdf
Kai-Fu Lee, Sanjoy Mahajan (1988). A Pattern Classification Approach to Evaluation Function Learning. Artificial Intelligence, Vol. 36, No. 1
Dap Hartmann (1989). Notions of Evaluation Functions Tested against Grandmaster Games. Advances in Computer Chess 5
Maarten van der Meulen (1989). Weight Assessment in Evaluation Functions. Advances in Computer Chess 5
Bruce Abramson (1989). On Learning and Testing Evaluation Functions. Proceedings of the Sixth Israeli Conference on Artificial Intelligence, 1989, 7-16.
Danny Kopec, Ed Northam, David Podber, Yehya Fouda (1989). The Role of Connectivity in Chess. Workshop on New Directions in Game-Tree Search, pdf

1990 ...

Bruce Abramson (1990). On Learning and Testing Evaluation Functions. Journal of Experimental and Theoretical Artificial Intelligence 2: 241-251.
Ron Kalnim (1990). A Positional Assembly Model. ICCA Journal, Vol. 13, No. 3
Paul E. Utgoff, Jeffery A. Clouse (1991). Two Kinds of Training Information for Evaluation Function Learning. University of Massachusetts, Amherst, Proceedings of the AAAI 1991
Ingo Althöfer (1991). An Additive Evaluation Function in Chess. ICCA Journal, Vol. 14, No. 3
Ingo Althöfer (1993). On Telescoping Linear Evaluation Functions. ICCA Journal, Vol. 16, No. 2 ^[6]
Alois Heinz, Christoph Hense (1993). Bootstrap learning of α-β-evaluation functions. ICCI 1993, pdf
Alois Heinz (1994). Efficient Neural Net α-β-Evaluators. pdf ^[7]
Peter Mysliwietz (1994). Konstruktion und Optimierung von Bewertungsfunktionen beim Schach. Ph.D. Thesis (German)
Don Beal, Martin C. Smith (1994). Random Evaluations in Chess. ICCA Journal, Vol. 17, No. 1
Yaakov HaCohen-Kerner (1994). Case-Based Evaluation in Computer Chess. EWCBR 1994
Michael Buro (1995). Statistical Feature Combination for the Evaluation of Game Positions. JAIR, Vol. 3
Peter Mysliwietz (1997). A Metric for Evaluation Functions. Advances in Computer Chess 8
Michael Buro (1998). From Simple Features to Sophisticated Evaluation Functions. CG 1998, pdf

2000 ...

Dan Heisman (2003). Evaluation Criteria, pdf from ChessCafe.com
Jeff Rollason (2005). Evaluation by Hill-climbing: Getting the right move by solving micro-problems. AI Factory, Autumn 2005 » Automated Tuning
Shogo Takeuchi, Tomoyuki Kaneko, Kazunori Yamaguchi, Satoru Kawai (2007). Visualization and Adjustment of Evaluation Functions Based on Evaluation Values and Win Probability. AAAI 2007
Omid David, Moshe Koppel, Nathan S. Netanyahu (2008). Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization, ACM Genetic and Evolutionary Computation Conference (GECCO '08), pp. 1469-1475, Atlanta, GA, July 2008.
Omid David, Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu (2009). Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions. ACM Genetic and Evolutionary Computation Conference (GECCO '09), pp. 1483 - 1489, Montreal, Canada, July 2009.
Omid David (2009). Genetic Algorithms Based Learning for Evolving Intelligent Organisms. Ph.D. Thesis.

2010 ...

Lyudmil Tsvetkov (2010). Little Chess Evaluation Compendium. 2010 pdf
Omid David, Moshe Koppel, Nathan S. Netanyahu (2011). Expert-Driven Genetic Algorithms for Simulating Evaluation Functions. Genetic Programming and Evolvable Machines, Vol. 12, No. 1, pp. 5--22, March 2011. » Genetic Programming
Jeff Rollason (2011). Mixing MCTS with Conventional Static Evaluation. AI Factory, Winter 2011 » Monte-Carlo Tree Search
Jeff Rollason (2012). Evaluation options - Overview of methods. AI Factory, Summer 2012
Lyudmil Tsvetkov (2012). An Addendum to a Little Chess Evaluation Compendium. Addendum June 2012 pdf, Addendum 2 September 2012 pdf, Addendum 3 September 2012 pdf, Addendum 4 November 2012 pdf, Addendum 5 November 27, 2012 pdf, Addendum 6 December 03, 2012 pdf
Lyudmil Tsvetkov (2012). Little Chess Evaluation Compendium. July 2012 pdf ^[8], December 03, 2012 pdf
Derek Farren, Daniel Templeton, Meiji Wang (2013). Analysis of Networks in Chess. Team 23, Stanford University, pdf

2015 ...

Nera Nesic, Stephan Schiffel (2016). Heuristic Function Evaluation Framework. CG 2016
Lyudmil Tsvetkov (2017). The Secret of Chess. amazon ^[9]
Lyudmil Tsvetkov (2017). Pawns. amazon

Blog & Forum Posts

1993 ...

Cray Blitz Evaluation by Robert Hyatt, rgc, March 05, 1993 » Cray Blitz
Mobility Measure: Proposed Algorithm by Dietrich Kappe, rgc, September 23, 1993 » Mobility
bitboard position evaluations by Robert Hyatt, rgc, November 17, 1994 » Bitboards

1995 ...

Value of the pieces by Joost de Heer, rgc, February 01, 1995
Evaluation function diminishing returns by Bruce Moreland, rgcc, February 1, 1997
Evaluation function question by Dave Fotland, rgcc, February 07, 1997
computer chess "oracle" ideas... by Robert Hyatt, rgcc, April 01, 1997 » Oracle
Evolutionary Evaluation by Dan Homan, rgcc, September 09, 1997 » Automated Tuning
Books that help for evaluation by Guido Schimmels, CCC, August 18, 1998
Static evaluation after the "Positional/Real Sacrifice" by Andrew Williams, CCC, December 03, 1999

2000 ...

Adding knowledge to the evaluation, what am I doing wrong? by Albert Bertilsson, CCC, March 13, 2003
testing of evaluation function by Steven Chu, CCC, April 17, 2003 » Engine Testing
Question about evaluation and branch factor by Marcus Prewarski, CCC, November 20, 2003 » Branching Factor
STATIC EVAL TEST (provisional) by Jaime Benito de Valle Ruiz, CCC, February 21, 2004 » Test-Positions

2005 ...

Re: Zappa Report by Ingo Althöfer, CCC, December 30, 2005
Do you evaluate internal nodes? by Tord Romstad, Winboard Forum, January 16, 2006 » Interior Node
question about symmertic evaluation by Uri Blass, CCC, May 23, 2007
Search or Evaluation? by Ed Schröder, Hiarcs Forum, October 05, 2007 » Search versus Evaluation, Search
Re: Search or Evaluation? by Mark Uniacke, Hiarcs Forum, October 14, 2007
Evaluation functions. Why integer? by oysteijo, CCC, August 06, 2008 » Float, Score
Evaluating every node? by Gregory Strong, CCC, January 03, 2009
Eval Dilemma by Edsel Apostol, CCC, April 03, 2009
Linear vs. Nonlinear Evalulation by Gerd Isenberg, CCC, August 26, 2009
Threat information from evaluation to inform q-search by Gary, CCC, September 15, 2009 » Quiescence Search

2010 ...

Correcting Evaluation with the hash table by Mark Lefler, CCC, February 05, 2010
Re: Questions for the Stockfish team by Milos Stanisavljevic, CCC, July 20, 2010
Most important eval elements by Tom King, CCC, September 17, 2010
Re: 100 long games Rybka 4 vs Houdini 1.03a by Tord Romstad, CCC, November 02, 2010
dynamically modified evaluation function by Don Dailey, CCC, December 20, 2010

2011

Suppose Rybka used Fruits evaluations by SR, Rybka Forum, August 29, 2011
writing an evaluation function by Pierre Bokma, CCC, December 27, 2011

2012

The evaluation value and value returned by minimax search by Chao Ma, CCC, March 09, 2012
Multi dimensional score by Nicu Ionita, CCC, April 20, 2012
Bi dimensional static evaluation by Nicu Ionita, CCC, April 20, 2012
Theorem proving positional evaluation by Nicu Ionita, CCC, April 20, 2012
log(w/b) instead of w-b? by Gerd Isenberg, CCC, May 02, 2012
The value of an evaluation function by Ed Schröder, CCC, June 11, 2012

2013

eval scale in Houdini by Rein Halbersma, CCC, January 14, 2013 » Houdini
An idea of how to make your engine play more rational chess by Pio Korinth, CCC, January 25, 2013
A Materialless Evaluation? by Thomas Kolarik, CCC, June 12, 2013
A different way of summing evaluation features by Pio Korinth, CCC, July 14, 2013 ^[10] ^[11]
Improve the search or the evaluation? by Jens Bæk Nielsen, CCC, August 31, 2013 » Search versus Evaluation
Multiple EVAL by Ed Schroder, CCC, September 22, 2013
floating point SSE eval by Marco Belli, CCC, December 13, 2013 » Float, Score

2014

5 underestimated evaluation rules by Lyudmil Tsvetkov, CCC, January 23, 2014
Thoughs on eval terms by Fermin Serrano, CCC, March 31, 2014

2015 ...

Value of a Feature or Heuristic by Jonathan Rosenthal, CCC, February 15, 2015
Couple more ideas by Lyudmil Tsvetkov, CCC, April 05, 2015
Most common/top evaluation features? by Alexandru Mosoi, CCC, April 10, 2015
eval pieces by Daniel Anulliero, CCC, June 15, 2015
* vs + by Stefano Gemma, CCC, July 19, 2015
(E)valuation (F)or (S)tarters by Ed Schröder, CCC, July 26, 2015

2016

Non-linear eval terms by J. Wesley Cleveland, CCC, January 29, 2016
A bizarre evaluation by Larry Kaufman, CCC, March 20, 2016
Chess position evaluation with convolutional neural network in Julia by Kamil Czarnogorski, Machine learning with Julia and python, April 02, 2016 » Deep Learning, Neural Networks
Calculating space by Shawn Chidester, CCC, August 07, 2016
Evaluation values help by Laurie Tunnicliffe, CCC, August 26, 2016
A database for learning evaluation functions by Álvaro Begué, CCC, October 28, 2016 » Automated Tuning, Learning, Texel's Tuning Method
Evaluation doubt by Fabio Gobbato, CCC, October 29, 2016

2017

Bayesian Evaluation Functions by Jonathan Rosenthal, CCC, February 15, 2017
improved evaluation function by Alexandru Mosoi, CCC, March 11, 2017 » Texel's Tuning Method, Zurichess
random evaluation perturbation factor by Stuart Cracraft, CCC, April 24, 2017
horrid positional play in a solid tactical searcher by Stuart Cracraft, CCC, April 29, 2017
Another attempt at comparing Evals ELO-wise by Kai Laskos, CCC, May 22, 2017 » Playing Strength
static eval in every node? by Erin Dame, CCC, June 09, 2017
comparing between search or evaluation by Uri Blass, CCC, October 09, 2017» Search
Neural networks for chess position evaluation- request by Kamil Czarnogorski, CCC, November 13, 2017 » Deep Learning, Neural Networks
AlphaGo's evaluation function by Jens Kipper, CCC, November 26, 2017
Logarithmic Patterns In Evaluations by Dennis Sceviour, CCC, December 09, 2017

2018

replace the evaluation by playing against yourself by Uri Blass, CCC, January 25, 2018 » Fortress

External Links

Mathematical Foundations

Chess Evaluation

References

^ Tableaux ayant pour sujet les échecs
^ Claude Shannon (1949). Programming a Computer for Playing Chess. pdf
^ Re: Linear vs. Nonlinear Evalulation by Tord Romstad, CCC, August 27, 2009
^ Re: Linear vs. Nonlinear Evalulation by Robert Hyatt, CCC, August 27, 2009
^ Re: Books that help for evaluation by Robert Hyatt, CCC, August 18, 1998
^ Re: Zappa Report by Ingo Althöfer, CCC, December 30, 2005
^ Re: Evaluation by neural network ? by Jay Scott, CCC, November 10, 1997
^ An Update of the Addendum to the LittleCompendium by Lyudmil Tsvetkov, CCC, July 02, 2012
^ The Secret of Chess by Lyudmil Tsvetkov, CCC, August 01, 2017
^ Euclidean distance from Wikipedia
^ Principal component analysis from Wikipedia
^ The value of an evaluation function by Ed Schröder, CCC, June 11, 2012
^ (E)valuation (F)or (S)tarters by Ed Schröder, CCC, July 26, 2015

Up one level

Wikispaces : chessprogramming - items matching description

Analog Evaluation

Table of Contents

Summing Amplifier

See also

Publications

Forum Posts

External Links

References

What links here?

Asymmetric evaluation

Table of Contents

See also

Publications

Forum Posts

1997 ...

2000 ...

2010 ...

References

What links here?

Automated Tuning

Table of Contents

Playing Strength

Parameter

Mathematical Optimization

Methods

Instances

Advantages

Disadvantages

Reinforment Learning

Instances

Engines

Supervised Learning

Move Adaption

Value Adaption

Advantages

Disadvantages

Regression

Linear Regression

Logistic Regression

Instances

See also

Publications

1959

1960 ...

1970 ...

1980 ...

1985 ...

1990 ...

1995 ...

2000 ...

2005 ...

2010 ...

2015 ...

Forum Posts

1997 ...

2000 ...

2005 ...

2010 ...

2015 ...

External Links

Optimization

Machine Learning

Statistics/Regression Analysis

Code

Misc

References

What links here?

Backward Pawn

Table of Contents

See also

Publications

Forum Posts

2000 ...

2010 ...

External Links

References

What links here?

Bad bishop

Table of Contents