Qualitative simulation predicts the set of possible behaviors...

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1 This paper describes a compositional modeling system called CDME (Collaborative Device Modeling Environment) for constructing domain theories of physical systems, composing models of devices, and simulating their behavior. We have implemented the system with the goal of encouraging sharing as well as the collaborative construction of knowledge bases describing physical domains. To maximize the chance of sharing and reuse of knowledge, CDME is implemented as a collection of network services on the World Wide Web. Knowledge is represented at three distinct levels: the physical, ontological, and logical. We describe the levels of representation, and how the system enables knowledge sharing at each level. 1 Introduction Compositional modeling [1] is an effective method for automatically formulating a behavior model of a complex physical system. In compositional modeling, a system is provided with a knowledge base about the physical world, including knowledge of the behavior of a varie...

this paper, we survey a wide range of research in temporal representation and reasoning, without committing ourselves to the point of view of any speci c application

In this paper, we revisit the problem of inducing a process model from time-series data.

This document describes a compositional modeling language, CML, which is a general declarative modeling language for logically specifying the symbolic and mathematical properties of the structure and behavior of physical systems. CML is intended to facilitate model sharing between research groups, many of which have long been using similar languages. These languages are based primarily on the language originally defined by Qualitative Process theory [Forbus 1984] and include the languages used for the Qualitative Physics Compiler (QPC) [Crawford 1990; Farquhar 1993; Farquhar 1994], compositional model formulation [Falkenhainer 1991] , and the Device Modeling Environment (DME) [Low and Iwasaki 1993] . CML is an attempt to synthesize and provide a clean redesign of these languages. 1. Introduction Compositional modeling is an effective paradigm for formulating a behavior model of physical system by composing descriptions of symbolic and mathematical properties of individual system compo...

Qualitative simulation is often a useful tool for studying the behavior of physical systems and has promise for providing automatic explanations of their behavior. However, in some cases it can overwhelm with detail. Behavior graphs with hundreds or thousands of states may obscure the basic patterns of behavior that a qualitative model was intended to explore. This paper describes an approach to comprehending complex behavior graphs by abstracting the behavior graph according to user-specified criteria that are simple and natural to provide. We present properties that an abstraction should meet to be faithful to the original behavior graph, prove necessary and sufficient operational conditions for an abstraction method to maintain these properties, and present a simple algorithm that incorpora...

The paper discusses an approach to construct discrete abstractions of hybrid systems by means of qualitative reasoning. The work is performed in the context of a modeling language for hybrid systems Charon.Weintroduce a qualitativeversion of the language and describe the abstraction technique using a motivational example. The resulting abstract model is conservative and can be used to analyze properties of the original hybrid system.

In this paper, we propose a practical framework for characterizing, evaluating and selecting reformulation techniques for reasoning about physical systems, with the long-term goal of automating the selection and application of these techniques. We view reformulation as a mapping from one encoding of a problem to another. A problem-solving task is in turn accomplished by the application of a sequence of reformulations to an initial problem encoding to produce a final encoding that addresses the task. Our framework provides the terminology to specify the conditions under which a particular reformulation technique is applicable, the cost associated with performing the reformulation, and the effects of the reformulation with respect to the problem encoding. As such it provides the vocabulary to characterize the selection of a sequence of reformulation techniques as a planning problem. Our framework is sufficiently flexible to accommodate previously proposed properties and metrics for reformulation. We have used the framework to characterize a variety of reformulation techniques, three of which are presented in this paper.

This paper describes an application of recently developed qualitative reasoning techniques to complex, socio--economic allocation problems. We explain why we believe traditional optimization methods are inappropriate and how qualitative reasoning could overcome some of these shortcomings. A case study is presented where an authority is expected to devise a policy that satisfies certain constraints. We describe how sets of rules of thumb implementing such a policy can be analyzed and validated by the decision maker using a program which automatically builds and simulates qualitative models of the underlying dynamical system. Such a program constructs and simulates models from incomplete descriptions of initial states and functional relationships between variables. We show that it nevertheless gives sufficient information to the decision maker. 1 Introduction National and international authorities must make difficult policy decisions regarding socio--economic problems which are complex,...