Constraint-based generalization: learning game-playing plans from single examples
Steven Minton · National Conference on Artificial Intelligence · 1984
Constraint-based Generalization is a technique for deducing generalizations from a single example. We show how this technique can be used for learning tactical combinations in games and discuss an implementation which learns forced wins in tic-tac-toe, go-moku, and chess.