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22
Compositional Modeling: Finding the Right Model for the Job
, 1991
"... Faikenhainer, B. and K.D. Forbus, Compositional modeling: finding the right model for the job, Artificial Intelligence 51 ( 1991 ) 95143. ..."
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Cited by 239 (24 self)
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Faikenhainer, B. and K.D. Forbus, Compositional modeling: finding the right model for the job, Artificial Intelligence 51 ( 1991 ) 95143.
Using Incomplete Quantitative Knowledge in Qualitative Reasoning
 In Proc. of the Sixth National Conference on Artificial Intelligence
, 1988
"... Incomplete knowledge of the structure of mechanisms is an important fact of life in reasoning, commonsense or expert, about the physical world. Qualitative simulation captures an important kind of incomplete, ordinal, knowledge, and predicts the set of qualitatively possible behaviors of a mechanism ..."
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Cited by 80 (17 self)
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Incomplete knowledge of the structure of mechanisms is an important fact of life in reasoning, commonsense or expert, about the physical world. Qualitative simulation captures an important kind of incomplete, ordinal, knowledge, and predicts the set of qualitatively possible behaviors of a mechanism, given a qualitative description of its structure and initial state. However, one frequently has quantitative knowledge as well as qualitative, though seldom enough to specify a numerical simulation.
Qualitative and Quantitative Simulation: Bridging the Gap
 Artificial Intelligence
, 1997
"... Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semiquantitative simulation is to use numeric intervals to represe ..."
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Cited by 51 (1 self)
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Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semiquantitative simulation is to use numeric intervals to represent incomplete quantitative information. In this research we demonstrate semiquantitative simulation using intervals in an implemented semiquantitative simulator called Q3. Q3 progressively refines a qualitative simulation, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques. Q3's simulations are based on a technique we call step size refinement. While a pure qualitative simulation has a very coarse step size, representing the state of a system trajectory at relatively few qualitatively distinct states, Q3 interpolates newly expl...
A semiquantitiative physics compiler
 In Proceedings of the Eighth International Workshop on Qualitative Reasoning
, 1994
"... Predicting the behavior of physical systems is essential to both common sense and engineering tasks. It is made especially challenging by the lack of complete precise knowledge of the phenomena in the domain and the system being modelled. We present an implemented approach to automatically building ..."
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Cited by 34 (6 self)
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Predicting the behavior of physical systems is essential to both common sense and engineering tasks. It is made especially challenging by the lack of complete precise knowledge of the phenomena in the domain and the system being modelled. We present an implemented approach to automatically building and simulating qualitative models of physical systems. Imprecise knowledge of phenomenais expressed by qualitative representations of monotonic functions and variable values. Incomplete knowledge about the system is either inferred or alternative complete descriptions that will affect behavior are explored. The architecture and algorithms used support both effective implementation and formal analysis. The expressiveness of the modelling language and strength of the resulting predictions are demonstrated by substantial applications to complex systems.
A dynamic systems perspective on qualitative simulation
 Artificial Intelligence
, 1990
"... This paper examines qualitative simulation (QS) from the phase space perspective of dynamic systems theory. QS consists of two steps: transition analysis determines the sequence of qualitative states that a system traverses and global interpretation derives its longterm behavior. I recast transitio ..."
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Cited by 11 (1 self)
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This paper examines qualitative simulation (QS) from the phase space perspective of dynamic systems theory. QS consists of two steps: transition analysis determines the sequence of qualitative states that a system traverses and global interpretation derives its longterm behavior. I recast transition analysis as a search problem in phase space and replace the assorted transition rules with two algebraic conditions. The first condition determines transitions between arbitrarily shaped regions in phase space, as opposed to QS which only handles ndimensional rectangles. It also provides more accurate results by considering only the boundaries between regions. The second condition determines whether nearby trajectories approach a fixed point asymptotically. It obtains better results than QS by exploiting local stability properties. I recast global interpretation as a search for attractors in phase space and present a global interpretation algorithm for systems whose local behavior determines global behavior uniquely. 'This research was performed while I was in the Clinical Decision Making Group oftheM.I.T. Laboratory
Order of Magnitude Reasoning using Logarithms
 In Proceedings of KR92
, 1992
"... Converting complex equations into simpler, more tractable equations usually involves approximation. Approximation is usually done by identifying and removing insignificant terms, while retaining significant ones. The significance of a term can be determined by order of magnitude reasoning. In ..."
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Cited by 11 (1 self)
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Converting complex equations into simpler, more tractable equations usually involves approximation. Approximation is usually done by identifying and removing insignificant terms, while retaining significant ones. The significance of a term can be determined by order of magnitude reasoning. In this paper we describe NAPIER, an implemented order of magnitude reasoning system. NAPIER defines the order of magnitude of a quantity on a logarithmic scale, and uses a set of rules to propagate orders of magnitudes through equations. A novel feature of NAPIER is the way it handles nonlinear simultaneous equations, using linear programming in conjunction with backtracking. We show that order of magnitude reasoning in NAPIER is, in general, intractable and then discuss an approximate reasoning technique that allow it to run fast in practice. Some of NAPIER's inference rules are heuristic, and we estimate the error introduced by their use. 1 INTRODUCTION Mathematical models ...
TaskSystem Analysis Using SlopeParametric Hybrid Automata
, 1997
"... Slopeparametric hybrid automata (SPHA) are hybrid automata whose variables can have parametric slopes. SPHA are useful, in particular, for modeling taskcontrol systems in which the task speeds can be adjusted for meeting some safety requirement. In this paper, we present an example of parametr ..."
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Cited by 3 (1 self)
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Slopeparametric hybrid automata (SPHA) are hybrid automata whose variables can have parametric slopes. SPHA are useful, in particular, for modeling taskcontrol systems in which the task speeds can be adjusted for meeting some safety requirement. In this paper, we present an example of parametric analysis for a simple task system. We introduce a prototype verification tool that fully automates the analysis.
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...