Learning to Play a Highly Complex Game from Human Expert Games
2002
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.
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