Automated Model Selection Using Context-Dependent Behaviors

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 cons..

Reasoning about assumptions in graphs of models

Solving design and analysis problems in physical worlds requires the representation of large amounts of knowledge. Recently, there has been much interest in explicitly making assumptions to decompose this knowledge into smaller Models. A crucial aspect of problemsolving paradigms based on models is that they include methods to automatically, and efficiently, select and change models. We represent physical domains as Graphs of Models, where models are the nodes of the graph and the edges are the assumptions that have to be changed in going from one model to the other. This paper describes the methods used in the Graphs of Models paradigm for changing models. This knowledge can be represented qualitatively, permitting fast inference mechanisms that provide powerful model changing behaviors.