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.
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. MorphII is far more sophisticated than Morph, both in its implementation and in its intellectual background.