Setting up Large-Scale Qualitative Models

Brian Falkenhainer, Kenneth D. Forbus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Scopus citations

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 languageEnglish (US)
Title of host publicationProceedings of the 7th National Conference on Artificial Intelligence, AAAI 1988
PublisherAAAI Press
Pages301-306
Number of pages6
ISBN (Electronic)0262510553, 9780262510554
StatePublished - 1988
Event7th National Conference on Artificial Intelligence, AAAI 1988 - St. Paul, United States
Duration: Aug 21 1988Aug 26 1988

Publication series

NameProceedings of the 7th National Conference on Artificial Intelligence, AAAI 1988

Conference

Conference7th National Conference on Artificial Intelligence, AAAI 1988
Country/TerritoryUnited States
CitySt. Paul
Period8/21/888/26/88

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

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