Abstract
Language | English |
---|---|
Pages | 45-82 |
Number of pages | 37 |
Journal | Theoretical Computer Science |
Volume | 252 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2001 |
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Keywords
- computer games
- planning
- adversarial planning
- alpha-beta search
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Applying adversarial planning techniques to Go. / Willmott, S.; Richardson, J.; Bundy, A.; Levine, J.M.
In: Theoretical Computer Science, Vol. 252, No. 1, 2001, p. 45-82.Research output: Contribution to journal › Article
TY - JOUR
T1 - Applying adversarial planning techniques to Go
AU - Willmott, S.
AU - Richardson, J.
AU - Bundy, A.
AU - Levine, J.M.
PY - 2001
Y1 - 2001
N2 - Approaches to computer game playing based on alpha-beta search of the tree of possible move sequences combined with a position evaluation function have been successful for many games, notably Chess. Such approaches are less successful for games with large search spaces and complex positions, such as Go, and we are led to seek alternatives. One such alternative is to model the goals of the players, and their strategies for achieving these goals. This approach means searching the space of possible goal expansions, typically much smaller than the space of move sequences. Previous attempts to apply these techniques to Go have been unable to provide results for anything other than a high strategic level or very open game positions. In this paper we describe how adversarial hierarchical task network planning can provide a framework for goal-directed game playing in Go which is also applicable both strategic and tactical problems.
AB - Approaches to computer game playing based on alpha-beta search of the tree of possible move sequences combined with a position evaluation function have been successful for many games, notably Chess. Such approaches are less successful for games with large search spaces and complex positions, such as Go, and we are led to seek alternatives. One such alternative is to model the goals of the players, and their strategies for achieving these goals. This approach means searching the space of possible goal expansions, typically much smaller than the space of move sequences. Previous attempts to apply these techniques to Go have been unable to provide results for anything other than a high strategic level or very open game positions. In this paper we describe how adversarial hierarchical task network planning can provide a framework for goal-directed game playing in Go which is also applicable both strategic and tactical problems.
KW - computer games
KW - planning
KW - adversarial planning
KW - alpha-beta search
UR - http://dx.doi.org/10.1016/S0304-3975(00)00076-1
U2 - 10.1016/S0304-3975(00)00076-1
DO - 10.1016/S0304-3975(00)00076-1
M3 - Article
VL - 252
SP - 45
EP - 82
JO - Theoretical Computer Science
T2 - Theoretical Computer Science
JF - Theoretical Computer Science
SN - 0304-3975
IS - 1
ER -