ROSIE@XX.LCS.MIT.EDU (Rosemary B. Hegg) (09/26/86)
Date: Wednesday, October 1, 1986
Time: 2.45 pm....Refreshments
3.00 pm....Lecture
Place: NE43-512A
GENERIC TASKS IN KNOWLEDGE-BASED REASONING:
CHARACTERIZING AND DESIGNING EXPERT SYSTEMS AT THE
``RIGHT'' LEVEL OF ABSTRACTION
B. CHANDRASEKARAN
Laboratory for Artificial Intelligence Research
Department of Computer and Information Science
The Ohio State University
Columbus, Ohio 43210
We outline the elements of a framework for expert system design that
we have been developing in our research group over the last several years.
This framework is based on the claim that complex knowledge-based reasoning
tasks can often be decomposed into a number of @i(generic tasks each
with associated types of knowledge and family of control regimes). At
different stages in reasoning, the system will typically engage in
one of the tasks, depending upon the knowledge available and the state
of problem solving. The advantages of this point of view are manifold:
(i) Since typically the generic tasks are at a much higher level of abstraction
than those associated with first generation expert system languages,
knowledge can be acquired and represented directly at the level appropriate to
the information processing task. (ii) Since each of the generic tasks
has an appropriate control regime, problem solving behavior may be
more perspicuously encoded. (iii) Because of a richer generic vocabulary
in terms of which knowledge and control are represented, explanation of
problem solving behavior is also more perspicuous. We briefly
describe six generic tasks that we have found very useful in our
work on knowledge-based reasoning: classification, state abstraction,
knowledge-directed retrieval, object synthesis by plan selection and
refinement,
hypothesis matching, and assembly of compound hypotheses for
abduction.
Host: Prof. Peter Szolovits
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