Relationships Between Non-Monotonic Reasoning and Incremental Learning:
Preliminary Outline of Invited Talk (Mar. 23 1993)
by Benjamin N. Grosof
Abstract:
We observe that non-monotonic reasoning arises in many kinds and
aspects of incremental learning, including: induction, preference-type
bias, and shift-able bias; learning by being told; knowledge
assimilation / integration; knowledge-base refinement / theory
revision; explanation-based learning; and sensory perception. We
review what the field of formal non-monotonic logical knowledge
representation has to offer the subject of incremental learning. This
includes: algorithmic methods for inference and updating / revision;
formal properties and guarantees; and concepts such as default,
model-preference, and prioritization.
In our attached paper "Representing and Reasoning with Defaults For
Learning Agents", we discuss in more detail a number of knowledge
representation issues revolving around the use of defaults for
learning agents.
Last update: 1-8-98
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