A SURVEY ON THE PARALLEL DISTRIBUTED PROCESSING OF PRODUCTION SYSTEMS

The importance of production systems in artificial intelligence (AI) has been repeatedly demonstrated by a large number of expert systems. As the number and size of expert systems grow, there has however been an emerging obstacle in such AI applications: the large processing time. The need for faster execution of production systems has spurred research in both the software and hardware domains, including connectionist architectures. This paper surveys various aspects of parallel distributed processing of production systems. Approaches taken to date to solve the problems associated with production systems are classified here into three levels: the algorithmic level, the parallel implementation level, and the connectionist level. Several pattern matchers and multiple rule firing principles are presented to demonstrate the algorithm level improvement. Several parallel implementation efforts are surveyed along with experimental results on real machines or with simulators. The presentation of three different types of connectionist production systems (local, distributed, and hierarchical representation) completes this survey. Finally, we explore some potential avenues towards the implementation of a true asynchronous parallel production system.