Dynamic Bundling: Less Effort for More Solutions

Choueiry, Berthe Y.; Davis, Amy

doi:10.1007/3-540-45622-8_6

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…Other systems are able to build abstractions, in some cases taking into account dynamically occurring circumstances during their execution, but are very specific in the abstractions they build and the conditions under which abstractions are built. Choueiry & Davis (2002) is an example of this type of system. Finally, there are systems that search for good abstractions in a broad, and sometimes very diverse, fam-ily of possible abstractions (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…This allows CSP problems to be solved much more quickly because instead of considering all the different values in the domain it is necessary only to consider one representative of each class (Hubbe & Freuder 1989;Freuder 1991;Ellman 1993;Haselbö ck 1993;Choueiry et al 1995;Freuder & Sabin 1995;Weigel et al 1996;Weigel & Faltings 1997;Choueiry & Noubir 1998). Dynamic bundling is a technique for efficiently discovering equivalence relations during problem solving (Choueiry & Davis 2002). It has been shown to yield multiple solutions to a CSP with significantly less effort than is necessary to find a single solution.…”

Section: Constraint Processingmentioning

confidence: 99%

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Abstraction and reformulation in artificial intelligence

Holte

Choueiry

2003

Phil. Trans. R. Soc. Lond. B

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This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.

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Section: Discussionmentioning

confidence: 99%

Section: Constraint Processingmentioning

confidence: 99%

Abstraction and reformulation in artificial intelligence

Holte

Choueiry

2003

Phil. Trans. R. Soc. Lond. B

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“…The effectiveness of PW-AC was established on sparse networks and other structured benchmarks [22]. 4…”

Section: …mentioning

confidence: 99%

“…Further, Lecoutre et al propose the algorithm eSTR to enforce pairwise-consistency using only the size of the coarse blocks [20]. Our work is also related to the computation of subproblem interchangeability [4,5,7,18], where the variables' domains (instead of constraints' relations) are dynamically partitioned by the constraints of a specified subproblem.…”

Section: Introductionmentioning

confidence: 99%

Improving Relational Consistency Algorithms Using Dynamic Relation Partitioning

Schneider

Woodward

Choueiry

et al. 2014

Lecture Notes in Computer Science

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Relational consistency algorithms are instrumental for solving difficult instances of Constraint Satisfaction Problems (CSPs), often allowing backtrack-free search. In this paper, we improve an algorithm for enforcing relational consistency by exploiting the property that the constraints of the dual encoding of a CSP are piecewise functional. This property allows us to partition a CSP relation into blocks of equivalent tuples at varying levels of granularity. Our new algorithm dynamically exploits those partitions. Our experiments show a significant improvement of the processing time for enforcing relational consistency.

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Backtrack Search Techniques And Heuristics

Lecoutre

2011

Wiley Encyclopedia of Operations Research and Management Science

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Satisfying a set of constraints is known as an instance of the constraint satisfaction problem (CSP), which is the subject of intense research in both artificial intelligence and operations research. Practical solution of CSP instances usually involves backtrack search. This is a complete approach that performs an exhaustive exploration of the search space of the instance to be solved. There have been considerable efforts during the last three decades to maximize the practical efficiency of backtrack search, relying on the concepts of constraint propagation, intelligent backtracking, restarting policy, variable (and value) ordering heuristic, and so on. In particular, the development of general‐purpose adaptive heuristics has shown impressive progress in guiding search.

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Dynamic Bundling: Less Effort for More Solutions

Cited by 13 publications

References 8 publications

Abstraction and reformulation in artificial intelligence

Abstraction and reformulation in artificial intelligence

Improving Relational Consistency Algorithms Using Dynamic Relation Partitioning

Backtrack Search Techniques And Heuristics

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