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24
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 214 (21 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 69 (16 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 44 (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 32 (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.
Qualitative Models in Interactive Learning Environments: An Introduction
 INTRODUCTION TO A SPECIAL ISSUE OF INTERACTIVE LEARNING ENVIRONMENTS ON "THE USE OF QUALITATIVE REASONING TECHNIQUES IN INTERACTIVE LEARNING ENVIRONMENTS"
, 1998
"... ..."
A hierarchy of Constraint Systems for DataFlow Analysis of Constraint LogicBased Languages
 Science of Computer Programming
, 1999
"... Many interesting analyses for constraint logicbased languages are aimed at the detection of monotonic properties, that is to say, properties that are preserved as the computation progresses. Our basic claim is that most, if not all, of these analyses can be described within a unified notion of cons ..."
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Cited by 13 (7 self)
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Many interesting analyses for constraint logicbased languages are aimed at the detection of monotonic properties, that is to say, properties that are preserved as the computation progresses. Our basic claim is that most, if not all, of these analyses can be described within a unified notion of constraint domains. We present a class of constraint systems that allows for a smooth integration within an appropriate framework for the definition of nonstandard semantics of constraint logicbased languages. Such a framework is also presented and motivated. We then show how such domains can be built, as well as construction techniques that induce a hierarchy of domains with interesting properties. In particular, we propose a general methodology for domain combination with asynchronous interaction (i.e., the interaction is not necessarily synchronized with the domains' operations). By following this methodology, interesting combinations of domains can be expressed with all the the semantic el...
An Application of Constraint Propagation to DataFlow Analysis
 IN PROC OF NINTH IEEE CONFERENCE ON AI APPLICATIONS
, 1993
"... The optimized compilation of Constraint Logic Programming (CLP) languages can give rise to impressive performance improvements, even more impressive than the ones obtainable for the compilation of Prolog. On the other hand, the global analysis techniques needed to derive the necessary information ca ..."
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Cited by 11 (8 self)
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The optimized compilation of Constraint Logic Programming (CLP) languages can give rise to impressive performance improvements, even more impressive than the ones obtainable for the compilation of Prolog. On the other hand, the global analysis techniques needed to derive the necessary information can be significantly more complicated than in the case of Prolog. The original contribution of the present work is the integration of approximate inference techniques, well known in the field of artificial intelligence (AI), with an appropriate framework for the definition of nonstandard semantics of CLP. This integration turns out to be particularly appropriate for the considered case of the abstract interpretation of CLP programs over numeric domains. One notable advantage of this approach is that it allows to close the often existing gap between the formalization of dataflow analysis in terms of abstract interpretation and the possibility of efficient implementations. Towards this aim we i...
Lp, A Logic for Representing and Reasoning with Statistical Knowledge
, 1990
"... This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an importa ..."
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Cited by 11 (0 self)
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This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an important component of our world knowledge and that such knowledge is used in many different reasoning tasks. The work is further motivated by the observation that previous formalisms for representing probabilistic information are inadequate for representing statistical knowledge. The representation mechanism takes the form of a logic that is capable of representing a wide variety of statistical knowledge, and that possesses an intuitive formal semantics based on the simple notions of sets of objects and probabilities defined over those sets. Furthermore, a proof theory is developed and is shown to be sound and complete. The formalism offers a perspicuous and powerful representational tool for stat...
Generation of basic semialgebraic invariants using convex polyhedra
 Static Analysis: Proceedings of the 12th International Symposium, volume 3672 of Lecture Notes in Computer Science
"... Abstract. A technique for generating invariant polynomial inequalities of bounded degree is presented using the abstract interpretation framework. It is based on overapproximating basic semialgebraic sets, i.e., sets defined by conjunctions of polynomial inequalities, by means of convex polyhedra. ..."
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Cited by 9 (0 self)
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Abstract. A technique for generating invariant polynomial inequalities of bounded degree is presented using the abstract interpretation framework. It is based on overapproximating basic semialgebraic sets, i.e., sets defined by conjunctions of polynomial inequalities, by means of convex polyhedra. While improving on the existing methods for generating invariant polynomial equalities, since polynomial inequalities are allowed in the guards of the transition system, the approach does not suffer from the prohibitive complexity of the methods based on quantifierelimination. The application of our implementation to benchmark programs shows that the method produces nontrivial invariants in reasonable time. In some cases the generated invariants are essential to verify safety properties that cannot be proved with classical linear invariants. 1
Static Analysis of CLP Programs over Numeric Domains
 IN ACTES WORKSHOP ON STATIC ANALYSIS '92
, 1992
"... Constraint logic programming (CLP) is a generalization of the pure logic programming paradigm, having similar modeltheoretic, fixpoint and operational semantics [9]. Since the basic operational step in program execution is a test for solvability of constraints in a given algebraic structure, CLP ha ..."
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Cited by 7 (6 self)
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Constraint logic programming (CLP) is a generalization of the pure logic programming paradigm, having similar modeltheoretic, fixpoint and operational semantics [9]. Since the basic operational step in program execution is a test for solvability of constraints in a given algebraic structure, CLP has in addition an algebraic semantics. CLP is then a general paradigm which may be instantiated on various semantic domains, thus achieving a good expressive power. One relevant feature is the distinction between testing for solvability and computing a solution of a given constraint formula. In the logic programming case, this corresponds to the unification process, which tests for solvability by computing a solution (a set of equations in solved form or most general unifier ). In CLP, the computation of a solution of a constraint is left to a constraint solver, which does not affect the semantic definition of the language. This allows different computational domains, e.g. real arithmetic, to...