Advertisement

Discovery and Revision via Incremental Hill Climbing

  • Donald Rose
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 82)

Abstract

An important issue in machine learning concerns how initial discoveries learned in a domain must be revised over time to account for newly learned beliefs. When performing this task, existing truth maintenance systems, such as the TMS an ATMS, tend to keep too many alternate beliefs in memory. When searching for consistency among beliefs, and ATMS typically finds all solutions. ATMS finds only one, but beliefs not part of the current theory are still kept in memory in case they become current at a later time. Neither approach uses heuristics to decide which beliefs should be active, and thus in some domains they do too much work. In this paper I describe Revolver, a discovery system that conducts more reasoned belief revision through an incremental, hill-climbing approach. Amain assumption of the program is that all beliefs should not be stored over time; Revolver uses a heuristic evaluation function to decide how beliefs should be revised to account for new information. First, I will illustrate the program’s behavior with a detailed example from the domain of chemical discovery. An analysis of the system ‘s behaviour follows, with particular emphasis on issues pertaining to its belief revision process. Third, I compare Revolver to other belief revision approaches. Ideas are then presented for synthesizing these various methods, followed by a summary and closing remarks.

Keywords

Evaluation Function Belief Revision Water Reaction Default Assumption Hypothesis Type 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. de Kleer, J. (1984). Choices without Backtracking. Proceedings of the Fourth National Conference on Artificial Intelligence (pp. 79–85). Austin, TX: Morgan KaufmannGoogle Scholar
  2. Doyle, J. (1979). A Truth Maintenance System. Artificial Intelligence 12, pp.231–272.MathSciNetCrossRefGoogle Scholar
  3. Haiman, G. Bienkowski, M. A., Salem, K. & Pratt, I (1987). Measuring Change and Coherence in Evaluating Potential Change in View. Proceedings of the Ninth Annual Conference of the Cognitive Science Society (pp. 203–209). Seattle, WA: Lawrence Erlbaum Associates.Google Scholar
  4. Quine, W. V. & Ullian, J. S. (1978). The Web of Belief (Second Edition). New York, NY: Randon House.Google Scholar
  5. Rose, D. & Langley, P. (1986). Chemical Discovery as Belief Revision. Machine Learning 1,pp.423–451.Google Scholar
  6. Rose, D. & Langley, P. (1987). Belief Revision and Induction. Proceedings of the Ninth Annual Conference of the Cognitive Science Society. Seattle, WA: Lawrence Erlbaum Associates.Google Scholar
  7. Zytkow, J. M. & Simon, H. A. (1986). A Theory of Historical Discovery: The Construction of Componential Models. Machine Learning 1, pp.107–136.Google Scholar

Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Donald Rose
    • 1
  1. 1.Department of Information & Computer ScienceUniversity of CaliforniaIrvineUSA

Personalised recommendations