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Published byLambert Barrett Modified over 9 years ago
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Development of a Machine-Learning-Based AI For Go By Justin Park
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In 1997, IBM’s Deep Blue defeated Grand Master Gary Kasparov.
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The Future of AI Problem Solving: Artificial intelligence has been centered around Go Go is an ancient Board game developed in China from 2500-4000 years ago 19x19 Board Size Players alternate with black and white stones Game ends with two consecutive passes
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The Challenges of Go Large game set –200-300 possible moves –10,000,000,000 leaves in game tree Difficulty in creating a heuristic function Pattern analysis/abstract thinking
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The Solution: (My Project) A machine-learning-based AI with a genetic algorithm for “learning” new moves A minimalist heuristic “guiding function” for learning basic moves Database storing of previously played games Recreation of “Roving Eye” techniques to further adaptation to larger size boards.
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Development (Python) –Board rules: illegal moves and killing stones –Creation of heuristic function based on influence with respect to distance –Sort possible moves and corresponding score (as determined by evaluation function)
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Development (continued) Creation of classes Game and Games to store boards. Search for best move algorithm –Comparison of boards with similar # of moves –Heuristic function = similarity + influence(board)
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Results Machine Machine-Learning learns how to either win or lose Machine-Learning function degenerates when faced against its parent function Machine-Learning function improves with outside human intervention
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Future Work Research with a larger pool of heuristic functions Increase depth of heuristic search Compare boards with 3x3 squares Compatibility with GMP.sgf reading and writing
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