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SemiringBased Constraint Satisfaction and Optimization
 JOURNAL OF THE ACM
, 1997
"... We introduce a general framework for constraint satisfaction and optimization where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. The framework is based on a semiring structure, where the set of the semiring specifies the values to be asso ..."
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Cited by 194 (24 self)
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We introduce a general framework for constraint satisfaction and optimization where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. The framework is based on a semiring structure, where the set of the semiring specifies the values to be associated with each tuple of values of the variable domain, and the two semiring operations (1 and 3) model constraint projection and combination respectively. Local consistency algorithms, as usually used for classical CSPs, can be exploited in this general framework as well, provided that certain conditions on the semiring operations are satisfied. We then show how this framework can be used to model both old and new constraint solving and optimization schemes, thus allowing one to both formally justify many informally taken choices in existing schemes, and to prove that local consistency techniques can be used also in newly defined schemes.
Learning and Solving Soft Temporal Constraints: An Experimental Study
 In Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming
, 2002
"... Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences are associated with event distances and durations. However, sometimes such local preferences are dicult to set, and it may be easier instead to associate preferences to s ..."
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Cited by 11 (6 self)
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Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences are associated with event distances and durations. However, sometimes such local preferences are dicult to set, and it may be easier instead to associate preferences to some complete solutions of the problem, and then to learn from them suitable preferences over distances and durations.
SemiAutomatic Modeling by Constraint Acquisition
 In Francesca Rossi, editor, International Conference on Principles and Practice of Constraint Programming, number 2833 in LNCS
, 2003
"... Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. ..."
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Cited by 11 (3 self)
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Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning.
Experimental Results on Learning Soft Constraints
, 2000
"... Constraints are a very natural knowledge representation formalism. However, classical constraints (which are either satisfied or not) are not so flexible and cannot describe reallife features like preferences, costs, priorities, and uncertainties. Therefore recently many formalisms for soft cons ..."
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Cited by 10 (4 self)
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Constraints are a very natural knowledge representation formalism. However, classical constraints (which are either satisfied or not) are not so flexible and cannot describe reallife features like preferences, costs, priorities, and uncertainties. Therefore recently many formalisms for soft constraints (which can be satisfied at a certain level) have been developed. We address the problem of modeling a reallife problem by using soft constraints. In many reallife situations, one may know his/her preferences over some of the solutions, but have no idea on how to code this knowledge into the constraint problem in terms of local preferences, or also one may be able to give only a rough approximation of the desired levels of satisfaction of the constraints. We therefore suggest to treat the solution preferences as examples, and to employ a learning scheme which learns from such examples (either from scratch or from the available rough model) all the local preferences, so tha...
Preferences in constraint satisfaction and optimization
"... We review constraintbased approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle ..."
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Cited by 4 (0 self)
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We review constraintbased approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints.
Constraint acquisition as semiautomatic modeling
 In Proc. of AI’03
, 2003
"... Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint ne ..."
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Cited by 4 (4 self)
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Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint networks from sets of instances that are either acceptable solutions or nondesirable assignments of the problem we would like to express. Such an approach has the potential to be of assistance to a novice who is trying to articulate her constraints. By restricting the language of constraints used to build the network, this could also assist an expert to develop an efficient model of a given problem. This paper provides a theoretical framework for a research agenda in the area of interactive constraint acquisition, automated modelling and automated constraint programming. 1
Fuzzifying the Constraint Hierarchies Framework
 In Proceedings of the Fourth International Conference on Principles and Practices of Constraint Programming, LNAI 1520
, 1998
"... . The Constraint Hierarchy (CH) framework is used to tackle multiple criteria selection (MCS), consisting of a set of candidates and a set of, possibly competing, criteria for selecting the "best" candidate(s). In this paper, we identify aspects of the CH framework for further enhancement ..."
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Cited by 3 (1 self)
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. The Constraint Hierarchy (CH) framework is used to tackle multiple criteria selection (MCS), consisting of a set of candidates and a set of, possibly competing, criteria for selecting the "best" candidate(s). In this paper, we identify aspects of the CH framework for further enhancement so as to model and solve MCS problems more accurately. We propose the Fuzzy Constraint Hierarchies framework, which allows constraints to belong to, possibly, more than one level in a constraint hierarchy to a varying degree. We also propose to replace the standard equality relation = used in valuation comparators of the CH framework by the ffapproximate equality relation = a(ff) for providing more flexible control over the handling of valuations with close error values. These proposals result in three new classes of valuation comparators. Formal properties of the new comparators are given, wherever possible. 1 Introduction An overconstrained system [3] is a set of constraints with no solution, ca...
Constraint Solving and Programming: What Next?
 ACM Computing Surveys
, 1991
"... . In this paper we advocate for more flexible and userfriendly constraint solving environments, as well as for constraint programming languages which have great expressive power while maintaining a formal semantics based on few crucial concepts. We cite some of our work in these directions and we h ..."
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Cited by 2 (1 self)
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. In this paper we advocate for more flexible and userfriendly constraint solving environments, as well as for constraint programming languages which have great expressive power while maintaining a formal semantics based on few crucial concepts. We cite some of our work in these directions and we hint at subjects of our future research. 1. Constraint Solving Environments: The Next Generation The classical CSP (Constraint Satisfaction Problem) framework has been introduced formally at the beginning of the 70's (Montanari, 1974), and has been studied for about 20 years, during which many important results have been obtained. While the general problem is NP complete, one of the main research issues has been devoted to finding fast preprocessing algorithm that can make the search for a solution efficient in important practical cases. Such algorithms (Mackworth, 1977) (Montanari, 1974) (Freuder, 1978) (Dechter and Pearl, 1987) (Freuder, 1988) were found to be so convenient (Mackworth and ...
Notes for the ECAI2000 tutorial on Solving and Programming with Soft Constraints: Theory and Practice
, 2000
"... Soft constraints add to the classical notion of constraint the possibility of dealing with important features like fuzziness, uncertainty, optimization, probability, and partial satisfaction. This tutorial will describe the current stateoftheart in the area of soft constraints, by reviewing the e ..."
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Cited by 2 (0 self)
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Soft constraints add to the classical notion of constraint the possibility of dealing with important features like fuzziness, uncertainty, optimization, probability, and partial satisfaction. This tutorial will describe the current stateoftheart in the area of soft constraints, by reviewing the existing frameworks and pointing out the relations among them. Then, it will focus on one of the most general frameworks for soft constraints, which is based on a semiring structure, and, for such a framework, it will present its properties and local propagation algorithms. Finally, it will describe and show the usefulness of a programming language, called clp(fd,S), where soft constraints can be naturally used and are eciently implemented. This tutorial paper is intended to provide a coherent presentation of most of the material underlying the tutorial transparencies, that will be distributed to the audience at the conference site.