A Paper Abstract by Benjamin Grosof

Shift of Bias As Non-Monotonic Reasoning (1990)

by Benjamin N. Grosof and Stuart J. Russell

Abstract: We show how to express many kinds of inductive leaps, and shifts of bias, as deductions in a non-monotonic logic of {prioritized defaults}, based on circumscription. This continues our effort to view learning a concept from examples as an inference process, based on a declarative representation of biases, developed in (Russell & Grosof 1987, 1989). In particular, we demonstrate that "version space" bias can be encoded formally in such a way that it will be weakened when contradicted by observations. Implementation of inference in the non-monotonic logic then enables the principled, automatic modification of the description space employed in a concept learning program, which Bundy {et al.} (1985) named as "the most urgent problem facing automatic learning". We also show how to formulate with prioritized defaults two kinds of "preference" biases: maximal specificity and maximal generality. This leads us to a perspective on inductive biases as {preferred beliefs} (about the external environment). Several open questions remain, including how to implement efficiently the required non-monotonic theorem-proving, and how to provide the epistemological basis for default axioms and priorities among default axioms.
Last update: 1-8-98
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