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148
Partial Constraint Satisfaction
, 1992
"... . A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying ..."
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Cited by 427 (23 self)
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. A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying a maximal number of constraints. Standard backtracking and local consistency techniques for solving constraint satisfaction problems can be adapted to cope with, and take advantage of, the differences between partial and complete constraint satisfaction. Extensive experimentation on maximal satisfaction problems illuminates the relative and absolute effectiveness of these methods. A general model of partial constraint satisfaction is proposed. 1 Introduction Constraint satisfaction involves finding values for problem variables subject to constraints on acceptable combinations of values. Constraint satisfaction has wide application in artificial intelligence, in areas ranging from temporal r...
Semiringbased CSPs and Valued CSPs: Frameworks, Properties, and Comparison
 Constraints
, 1999
"... In this paper we describe and compare two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. While comparing the two ..."
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Cited by 102 (27 self)
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In this paper we describe and compare two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. While comparing the two approaches, we show how to pass from one to the other one, and we discuss when this is possible. The two frameworks have been independently introduced in [2], [3] and [35].
Logic Programming with Ordered Disjunction
 In Proceedings of AAAI02
, 2002
"... Logic programs with ordered disjunction (LPODs) combine ideas underlying Qualitative Choice Logic (Brewka, Benferhat, & Le Berre 2002) and answer set programming. Logic programming under answer set semantics is extended with a new connective called ordered disjunction. The new connective allows us t ..."
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Cited by 75 (7 self)
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Logic programs with ordered disjunction (LPODs) combine ideas underlying Qualitative Choice Logic (Brewka, Benferhat, & Le Berre 2002) and answer set programming. Logic programming under answer set semantics is extended with a new connective called ordered disjunction. The new connective allows us to represent alternative, ranked options for problem solutions in the heads of rules: A × B intuitively means: if possible A, but if A is not possible then at least B. The semantics of logic programs...
Temporal Constraint Reasoning with Preferences
, 2001
"... A number of reasoning problems involving the manipulation of temporal information can be viewed as implicitly inducing an ordering of decisions involving time (associated with durations or orderings of events) on the basis of preferences. For example, a pair of events might be constrained to o ..."
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Cited by 58 (9 self)
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A number of reasoning problems involving the manipulation of temporal information can be viewed as implicitly inducing an ordering of decisions involving time (associated with durations or orderings of events) on the basis of preferences. For example, a pair of events might be constrained to occur in a certain order, and, in addition, it might be preferable that the delay between them be as large, or as small, as possible. This paper explores problems in which a set of temporal constraints is specified, each with preference criteria for making local decisions about the events involved in the constraint. A reasoner must infer a complete solution to the problem such that, to the extent possible, these local preferences are met in the best way. Constraintbased temporal reasoning is generalized to allow for reasoning about temporal preferences, and the complexity of the resulting formalism is examined. While in general such problems are NPcomplete, some restrictions on the shape of the preference functions, and on the structure of the set of preference values, can be enforced to achieve tractability. In these cases, a generalization of a singlesource shortest path algorithm can be used to compute a globally preferred solution in polynomial time.
Introducing Variable Importance Tradeoffs into CPNets
 In Proceedings of UAI02
"... courses of action is a cornerstone of many AI applications, and usually this requires explicit information about the decisionmaker's preferences. ..."
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Cited by 56 (11 self)
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courses of action is a cornerstone of many AI applications, and usually this requires explicit information about the decisionmaker's preferences.
Preferencebased Constrained Optimization with CPnets
 Computational Intelligence
, 2001
"... Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based ..."
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Cited by 55 (10 self)
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Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering represented using a CPnetwork  a graphical model for representing qualitative preference information. This approach offers both pragmatic and computational advantages. First, it provides a convenient and intuitive tool for specifying the problem, and in particular, the decision maker's preferences. Second, it provides an algorithm for finding the most preferred feasible outcomes that has the following anytime property: the set of preferred feasible outcomes are enumerated without backtracking. In particular, the first feasible solution generated by this algorithm is optimal.
Soft Concurrent Constraint Programming
, 2001
"... . Soft constraints extend classical constraints to represent multiple consistency levels, and thus provide a way to express preferences, fuzziness, and uncertainty. While there are many soft constraint solving algorithms, even distributed ones, by now there seems to be no concurrent programming fram ..."
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Cited by 50 (32 self)
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. Soft constraints extend classical constraints to represent multiple consistency levels, and thus provide a way to express preferences, fuzziness, and uncertainty. While there are many soft constraint solving algorithms, even distributed ones, by now there seems to be no concurrent programming framework where soft constraints can be handled. In this paper we show how the classical concurrent constraint (cc) programming framework can work with soft constraints, and we also propose an extension of cc languages which can use soft constraints to prune and direct the search for a solution. We believe that this new programming paradigm, called soft cc (scc), can be very useful in many webrelated scenarios. In fact, the language level allows web agents to express their interaction and negotiation protocols, and also to post their requests in terms of preferences, and the underlying soft constraint solver can nd an agreement among the agents even if their requests are incompatible. 1
On graphical modeling of preference and importance
, 2006
"... In recent years, CPnets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CPnets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend ..."
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Cited by 50 (6 self)
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In recent years, CPnets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CPnets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend the CPnets formalism to handle another class of very natural qualitative statements one often uses in expressing preferences in daily life – statements of relative importance of attributes. The resulting formalism, TCPnets, maintains the spirit of CPnets, in that it remains focused on using only simple and natural preference statements, uses the ceteris paribus semantics, and utilizes a graphical representation of this information to reason about its consistency and to perform, possibly constrained, optimization using it. The extra expressiveness it provides allows us to better model tradeoffs users would like to make, more faithfully representing their preferences. 1.
Semiringbased Constraint Logic Programming: Syntax and Semantics
, 2001
"... this paper, more simply, soft constraint problems. ..."
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Cited by 44 (25 self)
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this paper, more simply, soft constraint problems.