The state of the art in parallel production systems

Abstract

The production system paradigm occupies a prominent place in artificial intelligence. Production systems have not yet been widely accepted in industry mainly due to their slow performance. Continuing research in knowledge processing requires larger and larger production systems, which would only exacerbate the performance problem. For this reason, it is important to apply parallel processing technology to production systems because it may provide the speed improvement necessary for future production systems. This paper examines recent research efforts in production systems. It begins by discussing the architecture of production systems and the cause of their slow performance. It groups the research efforts into three categories, faster sequential match algorithms, parallel match production systems, and multiple rule firing production systems, and analyzes the strength and weakness of each approach. A uniform terminology is used throughout the paper. By considering each category individually and comparing them collectively, a clear picture of recent research efforts in production systems is obtained.