Data-driven parallel production systems: a macro actor/token implementation

The importance of production systems in artificial intelligence has been repeatedly demonstrated by a number of expert systems. Much effort has therefore been expended on finding an efficient processing mechanism for processing production systems. Variable resolution actors called macro actors (a processing mechanism for production systems) are studied. A macro token, which is a collection of primitive data tokens, is introduced as a companion to macro actors. An approach to obtaining medium-grain parallelism, called macro actors tokens, is investigated. A set of guidelines is identified in the contest of production systems to derive well-formed macro actors from primitive macro actors. Parallel pattern matching is written in macro actors/tokens to be executed on a macro dataflow simulator. Simulation results demonstrate that the macro approach can be an efficient implementation of the production system paradigm