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MCαβ
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Table of Contents
Four Phases
See also
Publications
Forum Posts
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*
Search
* MCαβ
Monte-Carlo Alpha-Beta
,
(MCαβ)
is a
Best-First search
algorithm based on
Monte-Carlo Tree Search
and a shallow
alpha-beta
depth-first-search
. It is used in
Lines of Action
and in conjunction with
UCT
(Upper Confidence bounds applied to Trees) also in Chess. It is similar to the the Best-First Minimax-Search proposed by
Richard Korf
and
Max Chickering
[1]
.
Four Phases
MCαβ can be divided into four strategic phases, repeated as long as there is time left:
In the Selection phase the tree is traversed from the
root node
until it selects a
leaf node
that is not added to the tree yet
The Expansion strategy adds the leaf node to the tree
The Playout phase performs a shallow alpha-beta search
The Backpropagation strategy propagates the results through the tree
See also
Alpha-Beta
Minimax
Monte-Carlo Tree Search
MT-SSS*
Rollout Paradigm
Publications
Richard Korf
,
Max Chickering
(
1996
).
Best-First Minimax Search
.
Artificial Intelligence
, Vol. 84, No 1-2
Pim Nijssen
,
Mark Winands
(
2011
).
Playout Search for Monte-Carlo Tree Search in Multi-Player Games
.
Advances in Computer Games 13
,
pdf
Cameron Browne
,
Edward Powley
,
Daniel Whitehouse
,
Simon Lucas
,
Peter Cowling
,
Philipp Rohlfshagen
,
Stephen Tavener
,
Diego Perez
,
Spyridon Samothrakis
,
Simon Colton
(
2012
).
A Survey of Monte Carlo Tree Search Methods
.
IEEE Transactions on Computational Intelligence and AI in Games
, Vol. 4, No. 1
Hendrik Baier
,
Mark Winands
(
2013
).
Monte-Carlo Tree Search and minimax hybrids
.
CIG 2013
,
pdf
Hendrik Baier
,
Mark Winands
(
2014
).
Monte-Carlo Tree Search and Minimax Hybrids with Heuristic Evaluation Functions
.
ECAI CGW 2014
Bojun Huang
(
2015
).
Pruning Game Tree by Rollouts
.
AAAI
»
MCTS
,
MT-SSS*
,
Rollout Paradigm
[2]
Hendrik Baier
(
2017
).
A Rollout-Based Search Algorithm Unifying MCTS and Alpha-Beta
.
Computer Games
Forum Posts
MCTS without random playout?
by
Antonio Torrecillas
,
CCC
, January 01, 2012 »
B*
Help with Best-First Select-Formula
by
Srdja Matovic
,
CCC
, July 23, 2012
Re: Announcing lczero
by
Daniel Shawul
,
CCC
, January 21, 2018 »
LCZero
Alpha-Beta as a rollouts algorithm
by
Daniel Shawul
,
CCC
, January 25, 2018 »
Alpha-Beta
,
Monte-Carlo Tree Search
,
Scorpio
References
^
Richard Korf
,
Max Chickering
(
1996
).
Best-First Minimax Search
.
Artificial Intelligence
, Vol. 84, No 1-2
^
Re: Announcing lczero
by
Daniel Shawul
,
CCC
, January 21, 2018 »
LCZero
What links here?
Page
Date Edited
Alpha-Beta
Jan 28, 2018
Antonio Torrecillas
Mar 4, 2016
Daniel Shawul
Jan 28, 2018
GPU
Dec 16, 2017
MCαβ
Jan 28, 2018
Monte-Carlo Tree Search
Apr 26, 2018
Rocinante
Feb 19, 2018
Scorpio
Mar 28, 2018
Search
Feb 1, 2018
UCT
Jan 22, 2018
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Table of Contents
Monte-Carlo Alpha-Beta, (MCαβ)
is a Best-First search algorithm based on Monte-Carlo Tree Search and a shallow alpha-beta depth-first-search. It is used in Lines of Action and in conjunction with UCT (Upper Confidence bounds applied to Trees) also in Chess. It is similar to the the Best-First Minimax-Search proposed by Richard Korf and Max Chickering [1].
Four Phases
MCαβ can be divided into four strategic phases, repeated as long as there is time left:See also
Publications
Forum Posts
References
What links here?
Up one level