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Qualitative Simulation
 Artificial Intelligence
, 2001
"... Qualitative simulation predicts the set of possible behaviors... ..."
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Cited by 421 (31 self)
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Qualitative simulation predicts the set of possible behaviors...
Theories of Causal Ordering
 ARTIFICIAL INTELLIGENCE
, 1986
"... This paper is a response to Iwasaki and Sitnon 141 which criticizes de Kleer and Brown I~I.We argue that loans of tilt ir (run ions partu ularl ~ nun mint, tausaltts unodt lint, and stab,lzti, 0z lynaut troun the difference of concerns between engineering and economics. Our notion of causality arise ..."
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Cited by 27 (0 self)
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This paper is a response to Iwasaki and Sitnon 141 which criticizes de Kleer and Brown I~I.We argue that loans of tilt ir (run ions partu ularl ~ nun mint, tausaltts unodt lint, and stab,lzti, 0z lynaut troun the difference of concerns between engineering and economics. Our notion of causality arises from considerzng the interconnecuions of components not equations. When no fet’dhack is present. the ordering produced by our qualitative physics is similar to theirs. However, whenfeedback is present, our qualitative physics determines a causal ordering around feedback loops as well. Causal ordering is a general technique not out/v applicable to qualitative reasoning. Therefore we also explore the relations/zip between causal ordering and propagation of constraints upon which the unelhod5 of qualutatiic ph vsics are based.
Taming intractable branching in qualitative simulation
 Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI87). Los
, 1987
"... Qualitative simulation of behavior from structure is a valuable method for reasoning about partially known physical systems. Unfortunately, in many realistic situations, a qualitative description of structure is consistent with an intractibly large number of behavioral predictions. We present two co ..."
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Cited by 26 (7 self)
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Qualitative simulation of behavior from structure is a valuable method for reasoning about partially known physical systems. Unfortunately, in many realistic situations, a qualitative description of structure is consistent with an intractibly large number of behavioral predictions. We present two complementary methods, representing different tradeoffs between generality and power, for taming an important case of intractible branching. The first method applies to the most general case of the problem. It changes the level of the behavioral description to aggregate an exponentially exploding tree of behaviors into a few distinct possibilities The second method draws on additional mathematical knowledge, and assumptions about the smoothness of partially known functional relationships, to derive a correspondingly stronger result. Higherorder derivative constraints are automatically derived by manipulating the structural constraint model algebraically, and applied to eliminate impossible branches These methods have been implemented as extensions to QSIM and tested on a substantial number of examples They move us significantly closer to the goal of reasoning qualitatively about complex physical systems
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...
Qualitative reasoning about fluids and mechanics
 UNIVERSITY
, 1993
"... Understanding people's commonsense knowledge about physical world is a fundamental problem in building intelligent systems. If this knowledge can be represented and used by computers, they can duplicate people's ability to understand and interact with the world. Qualitative physics is the attempt t ..."
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Cited by 9 (0 self)
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Understanding people's commonsense knowledge about physical world is a fundamental problem in building intelligent systems. If this knowledge can be represented and used by computers, they can duplicate people's ability to understand and interact with the world. Qualitative physics is the attempt to capture and formalize this knowledge. An important aspect of qualitative reasoning is the ability to derive the possible behaviors of a given physical system from the structure of the system, using minimal initial information. This thesis investigates qualitative domain theories and reasoning techniques which will enable computers to analyze the qualitative behaviors of physical systems which include both mechanical mechanisms and fluids, such as internal combustion engines and hydraulic lift pumps. We have developed a domain theory which integrates richer models of mechanics, fluids, and geometry than previous research in qualitative physics. These theories and inference techniques are embodied in QSA, a program that produces possible behaviors of physical systems.
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...
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.
systems
, 2012
"... Abstract: In this report, inspired by nonconstructive simulation developed in the qualitative reasoning field, we present a nonconstructive interval simulation algorithm for the simulation of dynamic systems. To perform this kind of simulation, we first recast two integration methods, which were o ..."
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Abstract: In this report, inspired by nonconstructive simulation developed in the qualitative reasoning field, we present a nonconstructive interval simulation algorithm for the simulation of dynamic systems. To perform this kind of simulation, we first recast two integration methods, which were originally used in traditional numerical simulation, and made them suitable for performing interval simulation in a nonconstructive manner. Then we proposed an iterative interval narrowing algorithm to control the growth of intervals during simulation. To achieve better accuracy and efficiency of the simulation, we designed several simulation modes to meet different requirements of various problems. The proposed simulation algorithm was theoretically studied in terms of its completeness, soundness, convergence, and stability. Finally two classical dynamic systems, as well as an electrical circuit model containing an algebraic loop, were used as test examples to demonstrate the validity of the proposed simulation approach.