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Adaptive Inference

  • Alberto Segre
  • Charles Elkan
  • Daniel Scharstein
  • Geoffrey Gordon
  • Alexander Russell
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 195)

Abstract

Automatically improving the performance of inference engines is a central issue in automated deduction research. This paper describes and evaluates mechanisms for speeding up search in an inference engine used in research on reactive planning. The inference engine is adaptive in the sense that its performance improves with experience. This improvement is obtained via a combination of several different learning mechanisms, including a novel explanation-based learning algorithm, bounded-overhead success and failure caches, and dynamic reordering and reformulation mechanisms. Experimental results show that the beneficial effect of multiple speedup techniques is greater than the beneficial effect of any individual technique. Thus a wide variety of learning methods can reinforce each other in improving the performance of an automated deduction system.

Keywords

Inference Engine Cache Size Domain Theory Speedup Technique Proof Tree 
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

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Alberto Segre
    • 1
  • Charles Elkan
    • 1
  • Daniel Scharstein
    • 1
  • Geoffrey Gordon
    • 1
  • Alexander Russell
    • 1
  1. 1.Department of Computer ScienceCornell UniversityIthaca

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