Pattern Recognition,
is used to assign a label to an input value [1] , for instance to apply classification in machine learning applications, i.e. to identify objects and images, as well as computer chess related pattern of chess positions in Cognitive Psychology and concerning evaluation and control of the search in computer chess. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform a "fuzzy" matching. In contrast, Pattern matching usually has to be exact.
Chess pattern range from simple properties of squares and pieces concerning occupancy and control, to a more complex interrelated sets of features. Recognizers are implemented with decision trees, neural networks, and fuzzy logics. In his ICCA Journal paper Fuzzy Production Rules in Chess, Peter W. Frey[3] proposed feature strings or sets of three types. Type-A features must match completely, type-B feature strings represent features which are usually but not always present, while type-C features are present occasionally but are highly diagnostic when available. Those features were intended to use at the root for an oracle approach.
Mikhail Moiseevich Bongard[6] (1967). Проблема Узнавания. The Problem of Recognition, Nauka Press, Moscow, appeared as Pattern Recognition in its 1970 English translation
Mikhail Moiseevich Bongard (1970). Pattern Recognition. Rochelle Park, N.J., Hayden Book Co., Spartan Books
Albert Zobrist (1970). Feature Extraction and Representation for Pattern Recognition and the Game of Go. Ph.D. Thesis (152 pp.), University of Wisconsin. Also published as technical report #85
Albert Zobrist (1970). A Pattern Recognition Program which uses a Geometry-Preserving Representation of Features. Technical Report #85, pdf
Jacques Pitrat (1976). A Program to Learn to Play Chess. Pattern Recognition and Artificial Intelligence, pp. 399-419. Academic Press Ltd. London, UK. ISBN 0-12-170950-7.
David Wilkins (1979). Using Patterns and Plans to Solve Problems and Control Search. Ph.D. thesis, Computer Science Dept, Stanford University, AI Lab Memo AIM-329
Max Bramer (1982). Pattern-Based Representations of Knowledge in the Game of Chess. International Journal of Man-Machine Studies, Vol. 16, pp. 439-448.
Ross Quinlan (1983). Learning efficient classification procedures and their application to chess end games. Machine Learning: An Artificial Intelligence Approach
Ivan Bratko (1985). Symbolic Derivation of Chess Patterns. Progress in Artificial Intelligence (eds. L. Steels and J.A. Campbell), pp. 281-290. Ellis Horwood Ltd., Chichester, UK.
Eduardo F. Morales (1992). Learning Chess Patterns. Inductive Logic Programming (ed. Stephen Muggleton), Academic Press, The Apic Series, London, UK
Steven Walczak (1992). Pattern-Based Tactical Planning. IJPRAI 6(5)
Steven Walczak, Douglas D. Dankel II (1993). Acquiring Tactical and Strategic Knowledge with a Generalized Method for Chunking of Game Pieces. International Journal of Intelligent Systems 8 (2), 249-270.
Fernand Gobet, Herbert Simon (1998). Pattern recognition makes search possible: Comments on Holding (1992). Psychological Research, Vol. 61, pdf[8]
Hitoshi Matsubara, Steven Walczak, Reijer Grimbergen (1998). Analysis of important patterns in Shogi. The 15th Annual Meeting of the Japanese Cognitive Science Society, (Nagoya, Japan), 136-137. (in Japanese/Kanji)
Erik van der Werf (1999). Non-linear target based feature extraction by diabolo networks. Masters thesis. Pattern Recognition Group, Department of Applied Physics, Faculty of Applied Sciences, Delft University of Technology, pdf
Christopher Chabris, Eliot Hearst (2003). Mentalizing, Pattern Recognition and Forward Search: Effects of Playing Speed and Sight of the Position on Grandmaster Chess Errors. Cognitive Science, Vol. 27
is used to assign a label to an input value [1] , for instance to apply classification in machine learning applications, i.e. to identify objects and images, as well as computer chess related pattern of chess positions in Cognitive Psychology and concerning evaluation and control of the search in computer chess. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform a "fuzzy" matching. In contrast, Pattern matching usually has to be exact.
Table of Contents
Chess Pattern
Chess pattern range from simple properties of squares and pieces concerning occupancy and control, to a more complex interrelated sets of features. Recognizers are implemented with decision trees, neural networks, and fuzzy logics. In his ICCA Journal paper Fuzzy Production Rules in Chess, Peter W. Frey [3] proposed feature strings or sets of three types. Type-A features must match completely, type-B feature strings represent features which are usually but not always present, while type-C features are present occasionally but are highly diagnostic when available. Those features were intended to use at the root for an oracle approach.Fianchetto
Outposts
Returning Bishop
Trapped Pieces
King Safety Pattern
Mate at a Glance
Chess Programs
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