A brief overview of over-constrained systems

Jampel, Michael

doi:10.1007/3-540-61479-6_16

Cited by 9 publications

(5 citation statements)

References 76 publications

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“…But, this approach addresses the problem in a different way from ours, by a hierarchy of degrees and some error and comparator functions to choose between different solutions (see [24] for a survey on over-constrained systems).…”

Section: Example 15mentioning

confidence: 98%

Possibilistic uncertainty handling for answer set programming

Pichon

Garcia

Stéphan

et al. 2006

Ann Math Artif Intell

164

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In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.

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Section: Example 15mentioning

confidence: 98%

Possibilistic uncertainty handling for answer set programming

Pichon

Garcia

Stéphan

et al. 2006

Ann Math Artif Intell

164

View full text Add to dashboard Cite

show abstract

“…Hower and Jacobi (1988) have dealt with the detection of the source of an inconsistency and provided a con¯ict resolution set of possible relaxations. Finally, Jampel (1996) has mentioned further material from a more general point of view. Th e ® rst entry I X V denotes a non-empty subset of variables whose value assignments are restricted by this constraint.…”

Section: Related Workmentioning

confidence: 98%

Constraint updating

Hower¹,

Jacobi²

1998

Journal of Experimental & Theoretical Artificial Intelligen

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In the areas of arti® cial intelligence and computer-aided design there are many fundamental problems that can be formulated and solved by constraint techniques. Th e problems are often of a dynamic nature in the sense that the original problem changes in the course of time ; furthermore, the granularity of the changes is often located on a tuple level. So, working on that level ensures maximal¯exibility and problem adequacy. Here, both the modi® cations and the recomputations of the aOE ected constraints are working on that level. W ith the NP-complete complexity of global constraint satisfaction in mind, it is desirable to decrease the average complexity of the consistency algorithm. This objective may be achieved by applying the changes incrementally to only the aOE ected parts of the network avoiding total recomputations wherever possible. Especially in applications with large domains and many solution tuples, the reduction of the computational complexity is enormous.

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“…Hower and Jacobi (1998) have dealt with CSP modi® cations on the ® ne-grained tuple level, to permit more or fewer solution tuples. Finally, Jampel (1996) has compiled additional material.…”

Section: Related Workmentioning

confidence: 99%

Fine-grained conflict resolution in constraint satisfaction problems

Jacobi¹

1998

Journal of Experimental & Theoretical Artificial Intelligen

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Constraint relaxation is the modi® cation of a constraint network such that the network permits more solutions ; for instance, a formerly inconsistent network may become consistent. M ost of the algorithms in this area try to localize constraints or even whole levels (of priority) of constraints that must be removed to allow global consistency. However, the problem when removing entire (levels of) constraints is the high degree of violation of the original problem. (It is often the case that constraints do not permit only a few tuples which are essential to form a globally consistent solution. Removing whole (levels of) constraints is much farther away from the original problem than additionally permitting just the necessary tuples.) Th e present approach does not work on a constraint level, but on a tuple level. Th is greatly reduces the distance of the modi® ed (relaxed) problem from the original (hard) one. In this paper an`intelligent' approach is pursued where only the constraints explicitly given are considered ; therefore, no redundant constraints are synthesized. Furthermore, only that part of the constraint network that is actually aOE ected by the modi® cations is recomputed which is especially eOE ective in the frequent cases when only a few tuples are changed.

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A brief overview of over-constrained systems

Cited by 9 publications

References 76 publications

Possibilistic uncertainty handling for answer set programming

Possibilistic uncertainty handling for answer set programming

Constraint updating

Fine-grained conflict resolution in constraint satisfaction problems

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