Title :
Expected-outcome: a general model of static evaluation
Author_Institution :
Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA
fDate :
2/1/1990 12:00:00 AM
Abstract :
The expected-outcome model, in which the proper evaluation of a game-tree node is the expected value of the game´s outcome given random play from that node on, is proposed. Expected outcome is considered in its ideal form, where it is shown to be a powerful heuristic. The ability of a simple random sampler that estimates expected outcome to outduel a standard Othello evaluator is demonstrated. The sampler is combined with a linear regression procedure to produce efficient expected-outcome estimators. Overall, the expected-outcome model of two-player games is shown to be precise, accurate, easily estimable, efficiently calculable, and domain-independent
Keywords :
artificial intelligence; game theory; Othello evaluator; artificial intelligence; decision making; expected-outcome model; game theory; game-tree node; heuristic; linear regression; Analytical models; Computational modeling; Decision making; Game theory; Learning systems; Linear regression; Machine learning; Minimax techniques; Performance evaluation; Probability;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on