The Role of Heuristics in Learning by Discovery: Three Case Studies

  • Douglas B. Lenat
Part of the Symbolic Computation book series (SYMBOLIC)


As artificial intelligence (AI) programs are called upon to exhibit increasingly complex behaviors, their builders are faced with the growing task of inserting more and more knowledge into the machine. One long-range solution is for the program, by itself, to learn via discovery. The first case study presented, am, demonstrates that new domains of knowledge can be developed mechanically by using heuristics. Yet as new domain concepts, facts, and conjectures emerge, specific new heuristics, or informal judgmental rules, are needed. They in turn can be discovered by using a body of heuristics for guidance. The second case study, eurisko, has already achieved some promising results in this endeavor. If this process—using heuristics to guide “learning by discovery”—is so powerful and simple, one wonders why, for instance, nature has not adopted an analogous mechanism to guide evolution. Indeed, the final part of the article is a speculation that evolution does function in that manner. In place of the conventional Darwinian process of random mutation, we hypothesize a more powerful plausible generation scheme.


Random Mutation Heuristic Rule Domain Concept Power Curve Task Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1983

Authors and Affiliations

  • Douglas B. Lenat
    • 1
  1. 1.Stanford UniversityUSA

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