Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning

  • Gerald DeJong
Conference paper

DOI: 10.1007/11871842_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)
Cite this paper as:
DeJong G. (2006) Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science, vol 4212. Springer, Berlin, Heidelberg

Abstract

Over the last twenty years AI has undergone a sea change. The once-dominant 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gerald DeJong
    • 1
  1. 1.Computer Science DepartmentUniversity of Illinois at Urbana 

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