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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14485
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| Title: | RULE-BASED VERSUS STRUCTURE-BASED MODELS FOR EXPLAINING AND GENERATING
EXPERT BEHAVIOR |
| Authors: | Dhar, Vasant
Pople, Harry E. |
| Issue Date: | Mar-1987 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-87-27 |
| Abstract: | Flexible representations are required in order to understand and
generate expert behavior. While production rules with quantifiers can
encode experiential knowledge, they often have assumptions implicit in
them, making them brittle in problem scenarios where these assumptions
do not hold. Qualitative models achieve flexibility by representing the
domain entities and their interrelationships explicitly. However, in
problem domains where assumptions underlying such models change
periodically, it is necessary to be able to synthesize and maintain
qualitative models in response to the changing assumptions. In this
paper, we argue for a representation that contains partial model
components that are synthesized into qualitative models containing
entities and relationships relevant to the domain. The model components
can be replaced and rearranged in response to changes in the task
environment. We have found this "model constructor" to be
useful in synthesizing models that explain and generate expert behavior,
and have explored its ability to support decision-making in the problem
domain of business resource planning, where reasoning is based on models
that evolve in response to changing external conditions or internal policies. |
| URI: | http://hdl.handle.net/2451/14485 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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