Cmpositional modeling: finding the right model for the job

Brian Falkenhainer*, Kenneth D Forbus

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

233 Scopus citations

Abstract

To represent an engineer's knowledge will require domain theories that are orders of magnitude larger than today's theories, describe phenomena at several levels of granularity, and incorporate multiple perspectives. To build and use such theories effectively requires strategies for organizing domain models and techniques for determining which subset of knowledge to apply for a given task. This paper describes compositional modeling, a technique that addresses these issues. Compositional modeling uses explicit modeling assumptions to decompose domain knowledge into semi-independent model fragments, each describing various aspects of objects and physical processes. We describe an implemented algorithm for model composition. That is, given a general domain theory, a structural description of a specific system, and a query about the system's behavior, the algorithm composes a model which suffices to answer the query while minimizing extraneous detail. We illustrate the utility of compositional modeling by outlining the organization of a large-scale, multi-grain, multi-perspective model we have built for engineering thermodynamics, and showing how the model composition algorithm can be used to automatically select the appropriate knowledge to answer questions in a tutorial setting.

Original languageEnglish (US)
Pages (from-to)95-143
Number of pages49
JournalArtificial Intelligence
Volume51
Issue number1-3
DOIs
StatePublished - Oct 1991

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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