Compositional Modeling: Finding the Right Model for the Job

Falkenhainer, B. and K.D . Forbus, Compositional modeling: finding the right model for the job, Artificial Intelligence 51 (1991) 95-143 . 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 .