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Rule-based systems are widely used in artificial intelligence for modeling intelligent behavior and building expert systems. Most rule-based programs, however, are extremely computation intensive and run quite slowly. The slow speed of execution has prohibited the use of rule-based systems in domains requiring high performance and real-time response. In(More)
Production systems designed to function and grow in environments that make large numbers of different, sometimes competing, and sometimes unexpected demands require support from their interpreters that is qualitatively different from the support required by systems that can be carefully hand crafted to function in constrained environments. In this paper we(More)
Soar is an attempt to realize a set of hypotheses on the nature of general intelligence within a single system. Soar uses a production system (rule based system) to encode its knowledge base. Its learning mechanism, chunking, adds productions continuously to the production system. The process of searching for relevant knowledge, matching, is known to be a(More)
Rule-based systems, on the surface, appear to be capable of exploiting large amounts of parallelism—it is possible to match each rule to the data memory in parallel. In practice, however, we show that the speed-up from parallelism is quite limited, less than 10-fold. The reasons for the small speed-up are: (1) the small number of rules relevant to(More)
Although production systems are appropriate for many applications in the artificial intelligence and expert systems areas, there are applications for which they are not fast enough to be used. If they are to be used for very large problems with severe time constraints, speed increases are essential. Recognizing that substantial further increases are not(More)
Until now, most results reported for parallelism in production systems (rule-based systems) have been simulation results-very few real parallel implementations exist. In this paper, we present results from our parallel implementation of OPS5 on the Encore multiprocessor. The implementation exploits very fine-grained parallelism to achieve significant(More)