A Paper Abstract by Benjamin Grosof

A Sketch of Autonomous Learning using Declarative Bias (1990)

by Stuart J. Russell and Benjamin N. Grosof

Abstract: This paper summarizes progress towards the construction of autonomous learning agents, in particular those that use existing knowledge in the pursuit of new learning goals. To this end, we show that the bias driving a concept-learning program can be expressed as a first-order sentence that reflects knowledge of the domain in question. We then show how the process of learning a concept from examples can be implemented as a derivation of the appropriate bias for the goal concept, followed by a first-order deduction from the bias and the facts describing the instances. Given sufficient background knowledge, the example complexity of learning can be considerably reduced. Shift of bias, certain kinds of "preference-type" bias, and noisy instance data can be handled by moving to a non-monotonic inference system (Grosof & Russell 1989a). We emphasize that learning can and should be viewed as an interaction between new experiences and existing knowledge.
Last update: 1-8-98
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