Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration

A Semantic Theory of Abstractions

ions P. Pandurang Nayak Recom Technologies, NASA Ames Research Center, MS 269-2 Moffett Field, CA 94035. [email protected] Alon Y. Levy AT&T Bell Laboratories AI Principles Research Department 600 Mountain Avenue, Room 2C-406 Murray Hill, NJ 07974. [email protected] Abstract In this paper we present a semantic theory of abstractions based on viewing abstractions as model level mappings. This theory captures important aspects of abstractions not captured in the syntactic theory of abstractions presented by Giunchiglia and Walsh [ 1992 ] . Instead of viewing abstractions as syntactic mappings, we view abstraction as a two step process: first, the intended domain model is abstracted and then a set of (abstract) formulas is constructed to capture the abstracted domain model. Viewing and justifying abstractions as model level mappings is both natural and insightful. This basic theory yields abstractions that are weaker than the base theory. We show that abstractions that a..

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..