predictive search (APS) is a new method by which systems can improve their search performance through experience. It is believed that the development of such methods is critical, as currently a tremendous number of computational results are potentially wasted by not integrating search and partial search results into the knowledge of a problem-solving system. In the APS model, pattern formation and associative recall are used to improve or replace search. In this paper, the theory, background, and motivations behind the model are presented and its application to two-player game-playing programs is discussed. In these programs, the system develops a knowledge base of patterns (boolean features) coupled with weights and, using pattern-oriented evaluation, performs only 1-ply search, yet competes respectably with programs that search more. The learning mechanism is a hybrid of machine learning and artificial intelligence techniques that have been successful in other settings. Specific examples and performance results are taken from the domains of Hexapawn, Tic-Tac-Toe, Pente, Othello, and chess.

4 multi-layer topological router for generating rubberband sketches is described.The router uses hierarchical top-down partitioning to perform global routing for all nets The local router uses a region connectivity graph to generate shortest-path rubber-band routing.