Development of a Machine-Learning-Based AI For Go

1. Development of a Machine-Learning-Based AI For Go By Justin Park

2. In 1997, IBM’s Deep Blue defeated Grand Master Gary Kasparov. >

3. 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

5. 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

7. 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.

8. 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)

9. 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)

10. 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

11. 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