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A Multimodel Methodology for Qualitative Model Engineering
 ACM Transactions on Modeling and Computer Simulation
, 1992
"... Qualitative models arising in the artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both co ..."
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Cited by 70 (31 self)
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Qualitative models arising in the artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually illunderstood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning. Categories and Subject Descriptors: I.2.4 [Artificial Intelligen...
Qualitative Simulation: Then and Now
, 1993
"... ion, Soundness, and Incompleteness Once the abstraction relations from ODEs to QDEs, and from continuously differentiable functions to qualitative behaviors, are carefully defined 1 , the mathematical results are relatively straightforward. We can view an ordinary differential equation solver as ..."
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Cited by 17 (1 self)
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ion, Soundness, and Incompleteness Once the abstraction relations from ODEs to QDEs, and from continuously differentiable functions to qualitative behaviors, are carefully defined 1 , the mathematical results are relatively straightforward. We can view an ordinary differential equation solver as a theoremprover for theorems of a special form: DiffEqs ` ODE State(t 0 ) ! Beh: (1) A qualitative simulation algorithm can also be viewed as a specialpurpose theoremprover: QSIM ` QDE QState(t 0 ) ! or(QBeh 1 ; : : : QBeh n ): (2) The soundness theorem says that when QSIM proves a theorem of form (2), it is true: that is, for any ODE described by the QDE, and State(t 0 ) described by QState(t 0 ), the solution Beh to the ODE is described by one of the qualitative behaviors, QBeh 1 ; : : : QBeh n . The constraint filtering algorithm makes the proof very simple: all possible real transitions from one qualitative state to the next are proposed, and only impossible ones are filtered out...
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...
Reasoning About Energy in Qualitative Simulation
, 1992
"... Qualitative modeling and simulation make it feasible to predict the possible behaviors of a mechanism consistent with an incomplete state of knowledge. Though qualitative simulation predicts all possible behaviors of a system, it can also produce spurious behaviors, i.e., behaviors that correspond t ..."
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Cited by 4 (1 self)
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Qualitative modeling and simulation make it feasible to predict the possible behaviors of a mechanism consistent with an incomplete state of knowledge. Though qualitative simulation predicts all possible behaviors of a system, it can also produce spurious behaviors, i.e., behaviors that correspond to no solution of any ordinary differential equation consistent with the qualitative model. A method for reasoning about energy is presented that eliminates an important source of spurious behaviors. This method is applied to an industrially significant mechanisma nonlinear, proportionalintegral controllermand show that qualitative simulation capSures the main qualitative properties of such a system, such as stability and zerooffset control. It is believed that this is a significant step toward the application of qualitative simulation to modelbased monitoring, diagnosis, and design of realistic mechanisms.
Constructing Functional Models of a Device from its Structural Description
, 1994
"... structural description : : : : : : : : : : : : : : : : : : : : 31 4.7 FR templates : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31 4.8 Component class hierarchy : : : : : : : : : : : : : : : : : : : : : : 32 4.9 Organizing SFFs : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 ..."
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Cited by 3 (0 self)
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structural description : : : : : : : : : : : : : : : : : : : : 31 4.7 FR templates : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31 4.8 Component class hierarchy : : : : : : : : : : : : : : : : : : : : : : 32 4.9 Organizing SFFs : : : : : : : : : : : : : : : : : : : : : : : : : : : : 33 4.10 Additional component features : : : : : : : : : : : : : : : : : : : : 33 4.11 The Task Definition : : : : : : : : : : : : : : : : : : : : : : : : : : 34 V The Algorithm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 35 5.1 Simplify the structural description : : : : : : : : : : : : : : : : : : 35 5.2 Analyze the structural description : : : : : : : : : : : : : : : : : : 36 5.2.1 Specialmethod : : : : : : : : : : : : : : : : : : : : : : : : : 37 5.2.2 Fetchmatchsffmethod : : : : : : : : : : : : : : : : : : : : 39 5.2.3 Consolidateanalyzemethod : : : : : : : : : : : : : : : : : : 44 5.2.4 Partitionmethod : : : : : : : : : : : : : : : : : : : : : : : : 45 5.2.5 Co...
Automated Model Generation and Simulation
, 1994
"... Understanding or predicting the behavior of a complex physical system requires the construction and execution of a model of the system. Such a model is often handcrafted by the person studying the system, and the modeling process is not formalized to be reusable by others. We describe a method ..."
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Cited by 3 (1 self)
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Understanding or predicting the behavior of a complex physical system requires the construction and execution of a model of the system. Such a model is often handcrafted by the person studying the system, and the modeling process is not formalized to be reusable by others. We describe a method which uses first principles to automatically create models and simulators for complex motions, and an implemented system called Oracle. Given a description of a problem involving a physical system, Oracle automatically identifies relevant model fragments, instantiates them for the particular entities and physical phenomena in the problem, composes the instantiated fragments to form a model, and executes the model. Knowledge of physical phenomena is represented with general model fragments which can be shared and reused by many models. Experimental results show that the method is capable of generating correct models of several different types of physical systems if enough doma...
Comparative Analysis and Qualitative Integral Representations
 Workshop on Qualitative Reasoning
, 1991
"... Comparative analysis is applied to a qualitative behavior of an incompletely known mechanism, to determine the effect of a given perturbation on the behavior as a whole. This class of inference is useful in diagnosis, design, planning, and generally for understanding the relations among a set of alt ..."
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Cited by 2 (1 self)
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Comparative analysis is applied to a qualitative behavior of an incompletely known mechanism, to determine the effect of a given perturbation on the behavior as a whole. This class of inference is useful in diagnosis, design, planning, and generally for understanding the relations among a set of alternate qualitative behaviors. Comparative analysis depends on information which is implicit, and relatively difficult to extract, from qualitative differential equations. By introducing the definite integral as a descriptive term linking qualitative variables and their landmarks, we show that the qualitative integral representation (QIR) makes the required information easily accessible. Inspired by observations of expert physicists, we have adopted an approach to inference that allows global algebraic manipulation of the QIR. Within this approach, comparative analysis can be decomposed into a search and algebraic manipulation problems. Several detailed examples are presented to clarify our m...
Closed Form Solution of Qualitative Differential Equations
"... Numerical simulation, phasespace analysis, and analytic techniques are three methods used to solve quantitative differential equations. Most work in Qualitative Reasoning has dealt with analogs of the first two techniques, producing capabilities applicable to a wide range of systems. Although poten ..."
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Numerical simulation, phasespace analysis, and analytic techniques are three methods used to solve quantitative differential equations. Most work in Qualitative Reasoning has dealt with analogs of the first two techniques, producing capabilities applicable to a wide range of systems. Although potentially ofbenefit, little has been done to provide closedform, analytic solution techniques for qualitative differential equations (QDEs). This paper presents one such technique for the solution of a class of ordinary linear and nonlinear differential equations. The technique is capable of deriving closedform descriptions of the qualitative temporal behavior represented by such equations. A language QFL for describing qualitative temporal behaviors is presented, and procedures and an implementationQDIFF that solves equations in this form are demonstrated. Various techniques have been described in the literature for inferring qualitative behavior of physical systems. The first techniques were based on simulation [De Kleer&Brown 84, Forbus 84, Kuipers 86]. Analogous to numerical simulation, these techniques compute the
Exhibiting the Behavior of TimeDelayed Systems via an Extension to Qualitative Simulation
"... Abstract—This paper presents an extension to qualitative simulation that enables a qualitative reasoning system to support variables that exhibit delayed reactions to their constraining functions. Information stored in the previous levels of the behavior tree is retrieved and used to constrain multi ..."
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Abstract—This paper presents an extension to qualitative simulation that enables a qualitative reasoning system to support variables that exhibit delayed reactions to their constraining functions. Information stored in the previous levels of the behavior tree is retrieved and used to constrain multiple delayed variables and to capture the timedelay behavior of the system. The extension is applicable to qualitative simulators that generate timestamped behaviors. In particular,this is implemented and integrated with the existing fuzzy qualitative simulation algorithm. Results of an example application of this extended algorithm are provided. Index Terms—Qualitative fuzzy simulation,qualitative reasoning,time delays. I.