Machine Learning
Machines that learn to play games
Machines that learn to play games
Machine learning in games: a survey
Machines that learn to play games
Disciple-COA: From Agent Programming to Agent Teaching
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Strategies for Explanation Patterns: Basic Game Patterns with Application to Chess
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Fox-ga: A genetic algorithm for generating and analyzing battlefield courses of action
Evolutionary Computation
CABOT: an adaptive approach to case-based search
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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When the number of possible moves in each state of a game becomes very high, standard methods for computer game playing are no longer feasible. We present an approach for learning to play such a game from human expert games. The high complexity of the action space is dealt with by collapsing the very large set of allowable actions into a small set of categories according to their semantic intent, while the complexity of the state space is handled by representing the states of collections of pieces by a few relevant features in a location-independent way. The state-action mappings implicit in the expert games are then learnt using neural networks. Experiments compare this approach to methods that have previously been applied to this domain.