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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