MODELING BEHAVIOR SEQUENCES: PRINCIPLES, PRACTICE, PROSPECTS
Date
1985-12-01
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Abstract
Many knowledge-based computer programs embody finite-state models
which represent procedural knowledge, and use them to predict or
interpret new observations. In accordance with prevailing methods of
constructing expert systems, such models are invariably created by the
knowledge engineer explicitly for the program's use. However, in most
cases it would be of great practical (and theorectical) value if the
program could infer models automatically from observations of behavior.
This paper takes a wide-ranging look at the problems involved in
building casual models of discrete sequences of symbols, and surveys
methods of modeling.
A key question for the future is the inference of goals and plans from
observations of behavior, for procedural representations are much less
flexible than the goal-oriented approach which people seem to favor.
This issue is discussed and some speculations are included on how goals
and plans might be inferred.
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Computer Science