Toward robust real-world inference: A new perspective on Explanation-Based Learning

Gerald Dejong

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Over the last twenty years AI has undergone a sea change. The oncedominant paradigm of logical inference over symbolic knowledge representations has largely been supplanted by statistical methods. The statistical paradigm affords a robustness in the real-world that has eluded symbolic logic. But statistics sacrifices much in expressiveness and inferential richness, which is achieved by first-order logic through the nonlinear interaction and combinatorial interplay among quantified component sentences. We present a new form of Explanation Based Learning in which inference results from two forms of evidence: analytic (support via sound derivation from first-order representations of an expert's conceptualization of a domain) and empirical (corroboration derived from consistency with real-world observations). A simple algorithm provides a first illustration of the approach. Some important properties are proven including tractability and robustness with respect to the real world.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML 2006 - 17th European Conference on Machine Learning, Proceedings
Number of pages12
ISBN (Print)354045375X, 9783540453758
StatePublished - 2006
Externally publishedYes
Event17th European Conference on Machine Learning, ECML 2006 - Berlin, Germany
Duration: Sep 18 2006Sep 22 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4212 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th European Conference on Machine Learning, ECML 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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