Marcella.Zaragoza@ISL1.RI.CMU.EDU.UUCP (01/30/87)
AI SEMINAR
TOPIC: Knowledge-Based Reasoning at the Right Level of Abstraction:
A Generic Task Toolkit
SPEAKER: B. Chandrasekaran
Laboratory for Artificial Intelligence Research
Department of Computer and Information Science
The Ohio State University
Columbus, Ohio 43210
PLACE: Wean Hall 5409
DATE: Tuesday, February 3, 1987
TIME: 3:30 pm
ABSTRACT:
The first part of the talk is a critique of the level of abstraction of much
of the current discussion on knowledge-based systems. It will be argued
that the discussion at the level of rules-logic-frames-networks is the
"civil engineering" level, and there is a need for a level of abstraction
that corresponds to what the discipline of architecture does for
construction of buildings. The constructs in architecture, viewed as a
language of habitable spaces, can be @i(implemented ) using the constructs
of civil engineering, but are not reducible to them. Similarly, the level
of abstraction that we advocate is the language of generic tasks, types of
knowledge and control regimes.
In the second part of the talk, I will outline the elements of a framework
at this level of abstraction for expert system design that we have been
developing in our research group over the last several years. 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 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.
Finally, we will describe how the above approach leads naturally to
a new technology: a toolbox which helps one to build expert systems
by using higher level building blocks. We will review the toolbox,
and outline what sorts of systems can be build using the toolbox,
and what advantages accrue from this approach.