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Computer Go : A Go player. Rohit Gurjar CS365 Project Proposal, IIT Kanpur rgurjar@iitk.ac.in Guided By – Prof. Amitabha Mukerjee. Introduction to the game. A two player board game Around 3000 years old Players have white and black stones

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computer go a go player

Computer Go : A Go player

Rohit Gurjar

CS365 Project Proposal, IIT Kanpur

rgurjar@iitk.ac.in

Guided By – Prof. Amitabha Mukerjee

introduction to the game
Introduction to the game
  • A two player board game
  • Around 3000 years old
  • Players have white and black stones
  • A stone (or a group) is captured if it is completely surrounded by enemy stones
  • Goal is to surround territories (empty points or enemy stones) by with one’s own stones

Image ref : http://www.sente.ch/software/goban/BoardBig.jpg

past work done
Past work done
  • A lot of work has been done to develop a Computer Go player
  • But the best Go programs are ranked only as 1-3 kyu (equivalent to a intermediate amateur)
  • Go is an extremely difficult game for computers to play well. Four main factor causes failure of standard AI techniques
    • The state space of the game of go is intractably huge.
    • The branching factor of go is intractably huge.
    • It is difficult to construct a heuristic function to evaluate board states.
    • Proper go strategy requires an extremely deep look-ahead.

Ref : A machine learning approach to computer Go (2007) – Jeffrey Bagdis

algorithms used for move generation
Algorithms used for Move Generation
  • Two components were necessary
    • Evaluation Function
    • A tree search
  • Finding a good evaluation function is very hard
  • Solution- Generate goals
  • Choose a specific EF related to one goal and associate a specific move generator with this goal. (Reduced complexity)
  • What if more than one goal is relevant to winning the game?

Computer Go, an AI oriented survey- Bruno Bouzy, Tristan Cazenave (2000)

tree search
Tree search
  • In classical games tree search uses the EF to find out the best possible move
  • High branching factor is a problem (~200)
  • Local TS
    • Selecting moves localized on a part of the board
    • Problems
      • Defining locality criterion
      • Given the results of local TS , reconstruction of the global result
      • Independence of local situations
tree search1
Tree search
  • Splitting the game into sub-games was proved to be useful in the endgame.*
  • Problem – finding independent sub-games
  • Alpha-Beta pruning.
    • as the tree being searched, cut off some sections of the tree from consideration, without at all reducing the optimality of accuracy of an exhaustive search.

* M.Müller, Decomposition search: A combinatorial games approach to game tree search, with applications to solving Go endgames (1999)

machine learning
Machine Learning
  • Previous algorithms are inadequate for developing a Go player.
  • Machine learning through neural network technique
  • A function(nonlinear) maps a vector(board state) to a scalar(0-1) which shows the probability of winning
  • The perceptron - simplified computational model of the biological neurons

Ref : A machine learning approach to computer Go (2007) – Jeffrey Bagdis

perceptron
Perceptron
  • A single perceptron can be trained to produce a certain output for a corresponding input by adjusting its input weights appropriately.
  • In order to achieve a more precise approximation, perceptrons can be connected together to form Multi-layer networks - perceptrons can be connected together to form multi-layer networks

Ref : A machine learning approach to computer Go (2007) – Jeffrey Bagdis

multilayer network
Multilayer Network

Ref : A machine learning approach to computer Go (2007) – Jeffrey Bagdis

conclusion
Conclusion
  • This approach seems effective at producing an approximation of a value function that can be used to play go.
  • But this value function is not by itself sufficient for strong play
  • Combination of this with some of the previously shown algorithms may give good results.