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Predicting the Drape of Woven Cloth Using Interacting Particles
, 1994
"... We demonstrate a physicallybased technique for predicting the drape of a wide variety of woven fabrics. The approach exploits a theoretical model that explicitly represents the microstructure of woven cloth with interacting particles, rather than utilizing a continuum approximation. By testing a cl ..."
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Cited by 121 (5 self)
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We demonstrate a physicallybased technique for predicting the drape of a wide variety of woven fabrics. The approach exploits a theoretical model that explicitly represents the microstructure of woven cloth with interacting particles, rather than utilizing a continuum approximation. By testing a cloth sample in a Kawabata fabric testing device, we obtain data that is used to tune the model's energy functions, so that it reproduces the draping behavior of the original material. Photographs, comparing the drape of actual cloth with visualizations of simulation results, show that we are able to reliably model the unique largescale draping characteristics of distinctly different fabric types. iii Figure 1.1: Draping cloth objects 1 Introduction The vast number of uses for cloth are mirrored in the extraordinary variety of types of woven fabrics. These range from the most exquisite fine silks, to the coarsest of burlaps, and are woven from such diverse fibers as natural wool and synth...
Probabilistic argumentation systems
 Handbook of Defeasible Reasoning and Uncertainty Management Systems, Volume 5: Algorithms for Uncertainty and Defeasible Reasoning
, 2000
"... Different formalisms for solving problems of inference under uncertainty have been developed so far. The most popular numerical approach is the theory of Bayesian inference [42]. More general approaches are the DempsterShafer theory of evidence [51], and possibility theory [16], which is closely re ..."
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Cited by 53 (33 self)
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Different formalisms for solving problems of inference under uncertainty have been developed so far. The most popular numerical approach is the theory of Bayesian inference [42]. More general approaches are the DempsterShafer theory of evidence [51], and possibility theory [16], which is closely related to fuzzy systems.
Constraint logic programming  an informal introduction
 LOGIC PROGRAMMING IN ACTION, NUMBER 636 IN LNCS
, 1992
"... Constraint Logic Programming (CLP) is a new class of programming languages combining the declarativity of logic programming with the efficiency of constraint solving. New application areas, amongst them many different classes of combinatorial search problems such as scheduling, planning or resource ..."
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Cited by 35 (8 self)
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Constraint Logic Programming (CLP) is a new class of programming languages combining the declarativity of logic programming with the efficiency of constraint solving. New application areas, amongst them many different classes of combinatorial search problems such as scheduling, planning or resource allocation can now be solved, which were intractable for logic programming so far. The most important advantage that these languages offer is the short development time while exhibiting an efficiency comparable to imperative languages. This tutorial aims at presenting the principles and concepts underlying these languages and explaining them by examples. The objective of this paper is not to give a technical survey of the current state of art in research on CLP, but rather to give a tutorial introduction and to convey the basic philosophy that is behind the different ideas in CLP. It will discuss the currently most successful computation domains and provide an overview on the different consi...
Constraint Simplification Rules
, 1992
"... In current constraint logic programming systems, constraint solving is hardwired in a "black box". We are investigating the use of logic programs to define and implement constraint solvers 1 . The representation of constraint evaluation in the same formalism as the rest of the program greatly fac ..."
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Cited by 17 (3 self)
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In current constraint logic programming systems, constraint solving is hardwired in a "black box". We are investigating the use of logic programs to define and implement constraint solvers 1 . The representation of constraint evaluation in the same formalism as the rest of the program greatly facilitates the prototyping, extension, specialization and combination of constraint solvers. In our approach, constraints are specified by definite clauses provided by a host language, while constraint evaluation is specified using multiheaded guarded clauses called constraint simplification rules (SiRs) 2 . SiRs define determinate conditional rewrite systems that express how conjunctions of constraints simplify. They have been used to encode a range of constraint solvers in our prototype implementation. Additionally, the definite clauses specifying a constraint can be evaluated in the host language, if the constraint is "callable" and no SiR can simplify it further. In this way our appr...
A Taxonomy of Parallel Strategies for Deduction
 Annals of Mathematics and Artificial Intelligence
, 1999
"... This paper presents a taxonomy of parallel theoremproving methods based on the control of search (e.g., masterslaves versus peer processes), the granularity of parallelism (e.g., fine, medium and coarse grain) and the nature of the method (e.g., orderingbased versus subgoalreduction) . We anal ..."
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Cited by 14 (1 self)
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This paper presents a taxonomy of parallel theoremproving methods based on the control of search (e.g., masterslaves versus peer processes), the granularity of parallelism (e.g., fine, medium and coarse grain) and the nature of the method (e.g., orderingbased versus subgoalreduction) . We analyze how the di#erent approaches to parallelization a#ect the control of search: while fine and mediumgrain methods, as well as masterslaves methods, generally do not modify the sequential search plan, parallelsearch methods may combine sequential search plans (multisearch) or extend the search plan with the capability of subdividing the search space (distributed search). Precisely because the search plan is modified, the latter methods may produce radically di#erent searches than their sequential base, as exemplified by the first distributed proof of the Robbins theorem generated by the Modified ClauseDi#usion prover Peersmcd. An overview of the state of the field and directions...
Refinement of Datalog Programs
, 1996
"... . Refinement operators are exploited to change in an automated way incorrect clauses of a logic program. In this paper, we present four refinement operators for Datalogprogramsand demonstrate that all of them meet the properties of local finiteness, properness, and completeness (ideality). Such oper ..."
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Cited by 13 (3 self)
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. Refinement operators are exploited to change in an automated way incorrect clauses of a logic program. In this paper, we present four refinement operators for Datalogprogramsand demonstrate that all of them meet the properties of local finiteness, properness, and completeness (ideality). Such operators are based on the quasiordering induced upon a set of clauses by the generalization model of qsubsumption under object identity. This model of generalization, as well as the four refinement operators have been implemented in a system for theory revision that proved effective in the area of electronic document classification. 1. Introduction In a logic framework for the inductive synthesis of logic programs, a fundamental problem is the definition of locally finite, proper, and complete (ideal) refinement operators. Indeed, when the aim is to develop incrementally a logic program, that should be correct with respect to its intended model at the end of the development process, it become...
Generalised Constraint Propagation Over the CLP Scheme
 Journal of Logic Programming
, 1992
"... Constraint logic programming is often described as logic programming with unification replaced by constraint solving over a computation domain. There is another, very different, CLP paradigm based on constraint satisfaction, where programdefined goals can be treated as constraints and handled using ..."
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Cited by 11 (4 self)
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Constraint logic programming is often described as logic programming with unification replaced by constraint solving over a computation domain. There is another, very different, CLP paradigm based on constraint satisfaction, where programdefined goals can be treated as constraints and handled using propagation. This paper proposes a generalisation of propagation, which enables it to be applied on arbitrary computation domains, revealing that the two paradigms of CLP are orthogonal, and can be freely combined. The main idea behind generalised propagation is to use whatever constraints are available over the computation domain to express restrictions on problem variables. Generalised propagation on a goal G requires that the system extracts a constraint approximating all the answers to G. The paper introduces a generic algorithm for generalised propagation called topological branch and bound which avoids enumerating all the answers to G. Generalised propagation over the Herbrand univers...
Connectionist Unification with a distributed Representation
 In IJCNN92
, 1992
"... This paper presents a connectionist unification strategy for a Prologsystem. The unification strategy utilizes a backpropnet (bpnet) and a distributed connectionist representation. The mostgeneralunifier is computed by the connectionist heuristic in constant time. To keep the system also efficie ..."
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Cited by 10 (2 self)
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This paper presents a connectionist unification strategy for a Prologsystem. The unification strategy utilizes a backpropnet (bpnet) and a distributed connectionist representation. The mostgeneralunifier is computed by the connectionist heuristic in constant time. To keep the system also efficient in space the distributed representation was choosen. It is possible to show that this representation is assoziative. Moreover this connectionist strategy allows to use semantics and context during the unification. The network is able to learn context sensitive strategies and unification with extensions like Eunification (unification under a certain equationaltheory E). So unification by a network becomes not only very cheap cause of the constant propagationtime of the bpnet but it gives also the ability to interpret its output as a mechanism for uncertainty. 1 Introduction Unification in general is a technique which utilizes substitutions of variables with adequate terms to make term...
Reasoning with finite set constraints
 In Proc. Int. Conference on Information Processing and Management of Uncertainty in KnowledgeBased Systems
, 1998
"... The language of propositional logic is sometimes not appropriate to model realworld problems. Therefore, finite set constraints are introduced. A variable of a finite set constraint takes exactly one value out of a given set of values whereas a propositional variable is either true or false. Althou ..."
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Cited by 9 (8 self)
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The language of propositional logic is sometimes not appropriate to model realworld problems. Therefore, finite set constraints are introduced. A variable of a finite set constraint takes exactly one value out of a given set of values whereas a propositional variable is either true or false. Although the class of realworld problems which can be described using the language of propositional logic is not enlarged, the language of finite set constraints often allows a shorter and more structured description. This additional structure can be exploited when methods of propositional logic are generalized to finite set constraint logic. Here the variable elimination method and the original algorithm of Abraham to calculate disjoint terms for formulas are generalized. Acknowledgments We wish to thank Prof. J. Kohlas for his helpful corrections and comments
Unification in Prolog by Connectionist Models
 In Proceedings of the Fourth Australian Conference on Neural Networks ACNN93
, 1993
"... This paper describes a connectionist unification strategy. We introduce an uncertainty measure for unification. A selforganizing feature map is used for learning a similarity measure and a relaxationnetwork is used for unification. The coding allows a precise representation of terms. In addition b ..."
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Cited by 6 (1 self)
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This paper describes a connectionist unification strategy. We introduce an uncertainty measure for unification. A selforganizing feature map is used for learning a similarity measure and a relaxationnetwork is used for unification. The coding allows a precise representation of terms. In addition beliefvalues may be used to introduce uncertainty. If strict unification without uncertainty is used, correctness, completeness, and terminating of the method can be proven. The proof may be seen as a argument for the adequacy of unification with uncertainty. The system uses linear time in worst case and for subproblems of unification, like matching or wordproblem, constant time. The described strategy is able to cope with an extension to unification: It need not to check whether a variable does refer to the whole of the term (infinite unification). Unification by this net is very efficient and offers the ability to cope with uncertain data. 1 Introduction In general unification is a tec...