MODEL FOR EXPLOITING ASSOCIATIVE MATCHING IN AI PRODUCTION SYSTEMS

A content-addressable model of production systems (CAMPUS) has been de veloped. The main idea is to achieve high execution performance in pro duction systems by exploiting the potential fine-grain data parallelis m. The facts and the rules of a production system are uniformly repres ented as content-addressable memory (CAM) tables. CAMPUS differs from other CAM-inspired models in that it is based on a non-state-saving an d 'lazy' matching algorithm. The production system execution cycle is represented by a small number of associative search operations over th e CAM tables. The number does not depend, or depends slightly, on the number of the rules and the number of the facts in the production syst em. The model makes possible efficient implementation of large product ion systems in fast CAM. An experimental CAMPUS realisation of the pro duction language CLIPS is also reported. The production systems execut ion time for a large number of processed facts is about 1000 times low er than the corresponding CLIPS execution time on a standard computer architecture.