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R1-soar: an experiment in knowledge-intensive programming in a problem-solving architecture.

P S Rosenbloom1, J E Laird, J McDermott

  • 1Departments of Computer Science and Psychology, Stanford University, Stanford, CA 94305.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces Soar, a production-system architecture for knowledge-intensive programming. Soar enables reasoning from first principles and learns expertise through a chunking mechanism, demonstrated on a computer-system configuration task.

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computer Science

Background:

  • Knowledge-intensive programming requires systems capable of complex reasoning.
  • General problem-solving architectures need efficient knowledge representation and acquisition.
  • Expert systems often rely on pre-programmed knowledge, limiting adaptability.

Purpose of the Study:

  • To present an experiment in knowledge-intensive programming using the Soar architecture.
  • To demonstrate Soar's ability to reason from first principles.
  • To explore automatic expertise acquisition through a chunking mechanism.

Main Methods:

  • Utilizing the Soar production-system architecture.
  • Encoding knowledge within problem spaces for first-principles reasoning.
  • Implementing a chunking mechanism for automatic rule learning.
  • Demonstrating the approach on the R1 computer-system configuration task.

Main Results:

  • Soar successfully reasons from first principles.
  • Expertise knowledge can be acquired through programming or automatic chunking.
  • The system effectively combines knowledge and search for problem-solving.
  • The approach is validated on a complex real-world task.

Conclusions:

  • Soar provides a robust architecture for knowledge-intensive programming.
  • Automatic learning mechanisms enhance the development of expert systems.
  • The integration of knowledge and search is crucial for versatile problem-solving.