Learning Models of Opponent's Strategy in Game Playing (1993)
| Venue: | IN PROCEEDINGS OF THE AAAI FALL SYMPOSIUM ON GAMES: PLANNING AND LEARNING |
| Citations: | 25 - 4 self |
BibTeX
@INPROCEEDINGS{Carmel93learningmodels,
author = {David Carmel and Shaul Markovitch},
title = {Learning Models of Opponent's Strategy in Game Playing},
booktitle = {IN PROCEEDINGS OF THE AAAI FALL SYMPOSIUM ON GAMES: PLANNING AND LEARNING},
year = {1993},
pages = {140--147},
publisher = {The AAAI Press}
}
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Abstract
Most activity in the area of game playing programs is concerned with efficient ways of searching game trees. There is substantial evidence that game playing involves additional types of intelligent processes. One such process performed by human experts is the acquisition and usage of a model of their opponent's strategy. This work studies the problem of opponent modelling in game playing. A simplified version of a model is defined as a pair of search depth and evaluation function. M*, a generalization of the minimax algorithm that can handle an opponent model, is described. The benefit of using opponent models is demonstrated by comparing the performance of M* with that of the traditional minimax algorithm. An algorithm for learning the opponent's strategy using its moves as examples was developed. Experiments demonstrated its ability to acquire very accurate models. Finally, a full model-learning game-playing system was developed and experimentally demonstrated to have advantage over ...
Keyphrases
game playing opponent model game playing program full model-learning game-playing system involves additional type simplified version efficient way intelligent process opponent modelling accurate model game tree work study substantial evidence traditional minimax algorithm human expert evaluation function search depth
