Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
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Effective reasoning about complex engineered devices requires device models that are both adequate for the task and computationally efficient. This paper presents a method for constructing simple and adequate device models by selecting appropriate models for each of the device's components. Appropriate component models are determined by the context in which the device operates. We introduce context-dependent behaviors (CDBs), a component behavior model representation for encapsulating contextual modeling constraints. We show how CDBs are used in the model selection process by exploiting constraints from three sources: the structural and behavioral contexts of the components, and the expected behavior of the device. We describe an implemented program for selecting a simplest adequate. model. The inputs are the structure of the device, the expected device behavior, and a library of CDBs. The output is a set of component CDBs forming a structurally and behaviorally consistent device model that achieves the expected behavior.