Using Hash Tables to Expedite Knowledge Reasoning in the General Game Playing Agent

DOI: 10.5176/978-981-08-7656-2ATAI2010-56

Authors: Xinxin Sheng and David Thuente

Abstract:

General game playing research focuses on designing automated agents that accept declarative logic description of arbitrary games at run time and are able to play efficiently without human intervention. The game information including the game states, the rules of the game, and the player's role in the game are all represented in logic relations. The general game playing agent uses knowledge representation and reasoning algorithms to analyze and play the game. We use hash table to significantly improve the reasoning performance. We provide experimental data on seven different games: small games like the single player game Maze, the strategy game Mini-Chess, the two player game Tic-Tac-Toe, the middle size board games ConnectFour and Chess Endgame, the large size game like Othello, and finally the three-player eCommerce game Farmer. In all of these scenarios, our agent has proven to significantly outperform the standard published Java player.