Advice and planning in chess endgames
Proc. of the international NATO symposium on Artificial and human intelligence
Practical Issues in Temporal Difference Learning
Machine Learning
Inductive logic programming with large-scale unstructured data
Machine intelligence 14
Learning to Play Chess Using Temporal Differences
Machine Learning
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Machine learning in games: a survey
Machines that learn to play games
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We propose an approach to the learning of long-term plans for playing chess endgames. We assume that a computer-generated database for an endgame is available, such as the king and rook vs. king, or king and queen vs. king and rook endgame. For each position in the endgame, the database gives the "value" of the position in terms of the minimum number of moves needed by the stronger side to win given that both sides play optimally. We propose a method for automatically dividing the endgame into stages characterised by different objectives of play. For each stage of such a game plan, a stage-specific evaluation function is induced, to be used by minimax search when playing the endgame. We aim at learning playing strategies that give good insight into the principles of playing specific endgames. Games played by these strategies should resemble human expert's play in achieving goals and subgoals reliably, but not necessarily as quickly as possible.