Abstract
A qualitative physics which captures the depth and breadth of an engineer's knowledge will be orders of magnitude larger than the models of today's qualitative physics. To build and use such models effectively requires explicit modeling assumptions to manage complexity. This, in turn, gives rise to the problem of selecting the right qualitative model for some purpose. This paper addresses these issues by describing a set of conventions for modeling assumptions. Simplifying assumptions decompose a domain into different grain sizes and perspectives which may be reasoned about separately. Operating assumptions reduce the complexity of qualitative simulation by focusing on particular behaviors of interest. We show how these assumptions can be directly represented in Qualitative Process theory, using a multi-grain, multi-slice model of a Navy propulsion plant for illustration. Importantly, we show that model selection can often be performed automatically via partial instantiation. We illustrate this technique with a simple explanation generation program that uses the propulsion plant model to answer questions about physical and functional characteristics of its operation.
Original language | English (US) |
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Title of host publication | Proceedings of the 7th National Conference on Artificial Intelligence, AAAI 1988 |
Publisher | AAAI Press |
Pages | 301-306 |
Number of pages | 6 |
ISBN (Electronic) | 0262510553, 9780262510554 |
State | Published - 1988 |
Event | 7th National Conference on Artificial Intelligence, AAAI 1988 - St. Paul, United States Duration: Aug 21 1988 → Aug 26 1988 |
Publication series
Name | Proceedings of the 7th National Conference on Artificial Intelligence, AAAI 1988 |
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Conference
Conference | 7th National Conference on Artificial Intelligence, AAAI 1988 |
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Country/Territory | United States |
City | St. Paul |
Period | 8/21/88 → 8/26/88 |
Funding
John Collins provided valuable commentary and technical assistance. This research was supported by an IBM Graduate Fellowship, by the National Aeronautics and Space Administration, Contract No. -NASA NAG-9137,, by the Office of Naval Research, Contract No. N00014-85-K-0225, and by an NSF Presidential Young Investigator Award.
ASJC Scopus subject areas
- Artificial Intelligence
- Software