new computational approach to the game of go
TRANSCRIPT
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A New ComputationalApproach to the Game of
Go
Amol Khedkar
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Aims
Investigate neural networksApplication of neural networks to GoLook at combining hard AI and soft AI with reference to GoAssess the success of such a combination
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What Is Go?
Ancient oriental game19 by 19 board of intersectionsPlayers take turns placing black and white stonesSurround territoryCapture opponents stones
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What Is Go?
Liberties
Capture
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Comparison With Chess
More moves to consider Average of 180 legal move choices
per turn
LookaheadLarger boardLarger search spaceTherefore brute force is impractical
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State of the Art
Many Faces Of Go – David FotlandBrute force search with all the trimmings: Minimax game tree search Alpha-Beta pruning Transposition table
Pattern matchingRule based expert system ~250 move suggesters
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Neural Networks
Modelled on the brain
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Neural Networks
Trained to recognise inputAbility to generaliseLearning Algorithms Back Propagation Network (BPN)
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Application to Go
Use neural network to suggest and score plausible movesUse Minimax search to investigate suggested plausible movesBenefits Fast and efficient move filter Look-ahead
Evaluation functions – liberty count
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Development
Manager functionMinimax implementation MTD(f) Alpha-Beta variation Iterative Deepening framework Transposition Table Best Move First Enhanced Transposition Cutoffs
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Development
Neural network implementation Training algorithm
BPN Training data Interface method – 81-45-1
architecture
Area Finder Network 361-90-9 architecture
Short range specialist networks
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Development
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Results
Successful combination of Alpha-Beta and neural networksPlayed against GNU Go Performance improved when using
combination of techniques
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Results
Configuration Score (we play black)
1. 9x9 Network B-8, W-49
2. 9x9 Network+Area Finder
B-3, W-21
3. Alpha-Beta B-9, W-12 (Not Complete)
4. 9x9 Network+Alpha-Beta
B-9, W-25
5. 9x9 Network+Alpha-Beta+Area Finder
B-7, W-18
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Conclusions
Human players and computer playersFilled in own territory, including eyes of safe groupsOpening game poor Short range networks promising
Evaluation function limits Alpha-Beta
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Future Work
Further development of different grain networks and different range and specialisationEvaluation function Temporal Difference network
Investigate alternative neural network architectures and algorithms