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SQsim: A Simulator for Imprecise ODE Models
 COMPUTERS AND CHEMICAL ENGINEERING 23 (1998) 2746
, 1998
"... This article describes a method for representing and simulating ordinary differential equation (ODE) systems which are imprecise  that is, where the ODE model contains both parametric and functional uncertainty. Such models, while useful in engineering tasks such as design and hazard analysis, are ..."
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Cited by 11 (3 self)
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This article describes a method for representing and simulating ordinary differential equation (ODE) systems which are imprecise  that is, where the ODE model contains both parametric and functional uncertainty. Such models, while useful in engineering tasks such as design and hazard analysis, are not used in practice because they require either special structures which limit the describable uncertainty or produce predictions which are extremely weak. This article describes SQSIM (for SemiQuantitative SIMulator), a system which provides a general language for representing and reasoning about many common forms of engineering uncertainty. By defining the model both qualitatively and quantitatively and by using a simulation method that combines inferences across the qualitativetoquantitative spectrum, SQSlM produces predictions that maintain a precision consistent with the model imprecision.
Comprehending Complex Behavior Graphs through Abstraction
 IN TENTH INTERNATIONAL WORKSHOP ON QUALITATIVE PHYSICS. AAAI TECHNICAL REPORT WS9601
, 1996
"... 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 ..."
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Cited by 9 (0 self)
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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 userspecified 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...
HigherOrder Derivative Constraints in Qualitative Simulation
 Artificial Intelligence
, 1991
"... Qualitative simulation is a useful method for predicting the possible qualitatively distinct behaviors of an incompletely known mechanism described by a system of qualitative differential equations (QDEs). Under some circumstances, sparse information about the derivatives of variables can lead to in ..."
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Cited by 7 (3 self)
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Qualitative simulation is a useful method for predicting the possible qualitatively distinct behaviors of an incompletely known mechanism described by a system of qualitative differential equations (QDEs). Under some circumstances, sparse information about the derivatives of variables can lead to intractable branching (or "chatter") representing uninteresting or even spurious distinctions among qualitative behaviors. The problem of chatter stands in the way of real applications such as qualitative simulation of models in the design or diagnosis of engineered systems. One solution to this problem is to exploit information about higherorder derivatives of the variables. We demonstrate automatic methods for identification of chattering variables, algebraic derivation of expressions for secondorder derivatives, and evaluation and application of the sign of second and thirdorder derivatives of variables, resulting in tractable simulation of important qualitative models. Caution is requir...
Refining Imprecise Models and Their Behaviors
 Austin
, 1996
"... v List of Tables x List of Figures xi Chapter 1 Introduction 1 1.1 The Need for Reasoning with Imprecision . . . . . . . . . . . . . . . . . . . 1 1.2 Existing Methods for Dealing With Imprecision and Why They are Insufficient 2 1.2.1 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . ..."
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v List of Tables x List of Figures xi Chapter 1 Introduction 1 1.1 The Need for Reasoning with Imprecision . . . . . . . . . . . . . . . . . . . 1 1.2 Existing Methods for Dealing With Imprecision and Why They are Insufficient 2 1.2.1 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Goals of this Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Summary of Key Contributions of this Research . . . . . . . . . . . . . . . 4 1.4.1 Nsim  A Numerical Simulator for Imprecise Models . . . . . . . . . 4 1.4.2 SQsim  A Semiquantitative Simulator for Imprecise Models . . . . 4 1.4.3 MSQUID  A Monotonic Function Estimator with Confidence Bands 4 1.4.4 SQUID  A System Identification Method Using Semiquantitative Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.5 Guide to this Dissertation . . . . . . . . . . . . . . . ....