The patterns are graphs of attack and defense relationships between pieces on the board and vectors of relative material difference between the players. To play, Morph uses one-ply search. For each position, it computes which patterns match the position and uses a global formula to combine the weights of the matching patterns into an evaluation. Morph plays the move with the best evaluation. Morph uses a wide variety of learning methods to learn the patterns and their weights.
Different methods add patterns, delete patterns, and set the weights.
Here are four sample games to show off Morph's play, with some analysis of Morph's strengths and weaknesses. APS Morph is an Adaptive-Predictive Search program. Morph only plays chess, but APS systems similar to Morph can tackle any discrete state-space search problem. MorphII can be seen as an improved, domain-independent outgrowth of Morph. It combines pattern scores hierarchically for evaluation, rather than globally as Morph does, for a big leap in expressiveness.