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ECML
2006
Springer

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

8 years 11 months ago
Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning
Abstract. 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 wo...
Gerald DeJong
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ECML
Authors Gerald DeJong
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