Performance evaluation of a simulated data-flow computer with low-resolution act
Journal of Parallel and Distributed Computing
Data-Driven Parallel Production Systems
IEEE Transactions on Software Engineering
TREAT: a new and efficient match algorithm for AI production systems
TREAT: a new and efficient match algorithm for AI production systems
Communications of the ACM
Parallelism in Production Systems
Parallelism in Production Systems
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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 to process production systems. While data-flow principles of execution offer the promise of high programmability for numerical computations, we study here variable resolution actors, called macro actors, a processing mechanism for production systems. Characteristics of the production system paradigm are identified, based on which we introduce the concept of macro tokens as a companion to macro actors. A set of guidelines is identified in the context of production systems to derive well-formed macro actors from primitive micro actors. Parallel pattern matching is written in macro actors/tokens to be executed on our Macro Data-flow simulator. Simulation results demonstrate that the macro approach can be an efficient implementation of production systems.