SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS

This chapter discusses some control strategies for artificial intelligence (AI) production systems. The overall computational efficiency of an AI production system depends upon where along the informed/uninformed spectrum the control strategy falls. The behavior of the control system as it makes rule selections can be regarded as a search process. Some examples of the ways in which the control system might search for a solution are the hill-climbing method of irrevocable rule selection, exploring a surface for a maximum, and the backtracking and graph-search regimes, search processes that permitted tentative rule selection. The chapter discusses the tentative control regimes especially with commutative production systems. Some of the search methods that developed for tentative control regimes can be adapted for use with certain types of commutative production systems using irrevocable control regimes.