Computer go a go player
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Computer Go : A Go player. Rohit Gurjar CS365 Project Proposal, IIT Kanpur [email protected] 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

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Computer go a go player

Computer Go : A Go player

Rohit Gurjar

CS365 Project Proposal, IIT Kanpur

[email protected]

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.


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