Temporal difference learning and TD-Gammon

• Gerald Tesauro

  IBM Thomas J. Watson Research Center, P.O. Box 704, Yorktown Heights, NY

抄録

Ever since the days of Shannon's proposal for a chess-playing algorithm [12] and Samuel's checkers-learning program [10] the domain of complex board games such as Go, chess, checkers, Othello, and backgammon has been widely regarded as an ideal testing ground for exploring a variety of concepts and approaches in artificial intelligence and machine learning. Such board games offer the challenge of tremendous complexity and sophistication required to play at expert level. At the same time, the problem inputs and performance measures are clear-cut and well defined, and the game environment is readily automated in that it is easy to simulate the board, the rules of legal play, and the rules regarding when the game is over and determining the outcome.

収録刊行物

• Communications of the ACM

  Communications of the ACM 38 (3), 58-68, 1995-03

  Association for Computing Machinery (ACM)

キーワード

• General Computer Science

詳細情報

• CRID: 1361418518917877248
• NII論文ID: 80008180967
• DOI: 10.1145/203330.203343
• ISSN: 15577317
• ISSN: 00010782
• ISSN: http://id.crossref.org/issn/00010782
• Web Site: https://dl.acm.org/doi/pdf/10.1145/203330.203343
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  • CiNii Articles