Learning to Play Chess Using Temporal Differences
Machine Learning
Learning to evaluate Go positions via temporal difference methods
Computational intelligence in games
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Temporal difference learning applied to a high-performance game-playing program
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
Chess-playing programs and the problem of complexity
IBM Journal of Research and Development
Evolution of heuristics for give-away checkers
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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In this paper we describe and analyze a Computational Intelligence (CI)-based approach to creating evaluation functions for two player mind games (i.e. classical turn-based board games that require mental skills, such as chess, checkers, Go, Othello, etc.). The method allows gradual, step-by-step training, starting with end-game positions and gradually moving towards the root of the game tree. In each phase a new training set is generated basing on results of previous training stages and any supervised learning method can be used for actual development of the evaluation function. We validate the usefulness of the approach by employing it to develop heuristics for the game of checkers. Since in previous experiments we applied it to training evaluation functions encoded as linear combinations of game state statistics, this time we concentrate on development of artificial neural network (ANN)- based heuristics.