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R1-Soar: An Experiment in Knowledge-Intensive Programming in a Problem-Solving Architecture

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5 Author(s)
Paul S. Rosenbloom ; Departments of Computer Science and Psychology, Stanford University, Stanford, CA 94305. ; John E. Laird ; John Mcdermott ; Allen Newell
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This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-7 ,  Issue: 5 )