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Pruning rules for constrained optimisation for conditional preferences

Pruning rules for constrained optimisation for conditional p...

引用

17th international conference on principles and practice of constraint programming, cp 2011

作者： Wilson, Nic Trabelsi, Walid Cork Constraint Computation Centre Department of Computer Science University College Cork Ireland

ISBN: (纸本)9783642237850

A depth-first search algorithm can be used to find optimal solutions of a constraint Satisfaction Problem (CSP) with respect to a set of conditional preferences statements (e.g., a cp-net). this involves checking at each leaf node if the corresponding solution of the CSP is dominated by any of the optimal solutions found so far;if not, then we add this solution to the set of optimal solutions. this kind of algorithm can clearly be computationally expensive if the number of solutions is large. At a node N of the search tree, with associated assignment b to a subset of the variables B, it may happen that, for some previously found solution α, either (a) α dominates all extensions of b;or (b) α does not dominate any extension of a. the algorithm can be significantly improved if we can find sufficient conditions for (a) and (b) that can be efficiently checked. In case (a), we can backtrack since we need not continue the search below N;in case (b), α does not need to be considered in any node below the current node N. We derive a sufficient condition for (b), and three sufficient conditions for (a). Our experimental testing indicates that this can make a major difference to the efficiency of constrained optimisation for conditional preference theories including cp-nets. © 2011, Springer-Verlag Berlin Heidelberg.

关键词： constraint satisfaction problems

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Solving Satisfaction Problems Using Large-Neighbourhood Search 26th

Solving Satisfaction Problems Using Large-Neighbourhood Sear...

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26th international conference on principles and practice of constraint programming, cp 2020

作者： Björdal, Gustav Flener, Pierre Pearson, Justin Stuckey, Peter J. Tack, Guido Department of Information Technology Uppsala University Uppsala Sweden Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9783030584740

Large-neighbourhood search (LNS) improves an initial solution, hence it is not directly applicable to satisfaction problems. In order to use LNS in a constraint programming (cp) framework to solve satisfaction problems, we usually soften some hard-to-satisfy constraints by replacing them with penalty-function constraints. LNS is then used to reduce their penalty to zero, thus satisfying the original problem. However, this can give poor performance as the penalties rarely cause propagation and therefore do not drive each cp search, and by extension the LNS search, towards satisfying the replaced constraints until very late. Our key observation is that entirely replacing a constraint is often overkill, as the propagator for the replaced constraint could have performed some propagation without causing backtracking. We propose the notion of a non-failing propagator, which is subsumed just before causing a backtrack. We show that, by only making a few changes to an existing cp solver, any propagator can be made non-failing without modifying its code. Experimental evaluation shows that non-failing propagators, when used in conjunction with penalties, can greatly improve LNS performance compared to just having penalties. this allows us to leverage the power of the many sophisticated propagators that already exist in cp solvers, in order to use LNS for solving hard satisfaction problems and for finding initial solutions to hard-to-satisfy optimisation problems. © 2020, Springer Nature Switzerland AG.

关键词： constraint satisfaction problems

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A characterisation of the complexity of forbidding subproblems in binary max-CSP

A characterisation of the complexity of forbidding subproble...

引用

18th international conference on principles and practice of constraint programming, cp 2012

作者： Cooper, Martin C. Escamocher, Guillaume Živný, Stanislav IRIT University of Toulouse III 31062 Toulouse France Department of Computer Science University of Oxford OX1 3QD Oxford United Kingdom

ISBN: (纸本)9783642335570

Tractable classes of binary CSP and binary Max-CSP have recently been discovered by studying classes of instances defined by excluding subproblems. In this paper we characterise the complexity of all classes of binary Max-CSP instances defined by forbidding a single subproblem. In the resulting dichotomy, the only non-trivial tractable class defined by a forbidden subproblem corresponds to the set of instances satisfying the so-called joint-winner property. © 2012 Springer-Verlag.

关键词： constraint theory

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Towards solver-independent propagators

Towards solver-independent propagators

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18th international conference on principles and practice of constraint programming, cp 2012

作者： Monette, Jean-Noël Flener, Pierre Pearson, Justin Department of Information Technology Uppsala University Uppsala Sweden

ISBN: (纸本)9783642335570

We present an extension to indexicals to describe propagators for global constraints. the resulting language is compiled into actual propagators for different solvers, and is solver-independent. In addition, we show how this high-level description eases the proof of propagator properties, such as correctness and monotonicity. Experimental results show that propagators compiled from their indexical descriptions are sometimes not significantly slower than built-in propagators of Gecode. therefore, our language can be used for the rapid prototyping of new global constraints. © 2012 Springer-Verlag.

关键词： constraint theory

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Applying decomposition methods to crossword puzzle problems

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11th international conference on principles and practice of constraint programming - cp 2005

作者： Zheng, Yaling Choueiry, Berthe Y. Constraint Systems Laboratory University of Nebraska Lincoln

ISBN: (纸本)3540292381

Structural decomposition methods have been proposed for identifying tractable constraint Satisfaction Problems (CSPs) [1-5]. the basic principle is to decompose a CSP into tree-structured sub-problems. the subproblems are solved independently, then the original CSP is solved in a backtrack-free manner after the tree structure is made arc-consistent, as described in [1]. In [5], we proposed four decomposition methods: HINGE+, CUT, TRAVERSE, and CaT and tested these methods on randomly generated CSPs. We compare these techniques on instances of the fully interlocked Crossword Puzzle Problems (cpPs) [6] taken from a public library [7] and identify special cases of the constraint hypergraphs where some decomposition techniques yield better results than others although in general the opposite holds. Our future work includes: 1) Identifying more such configurations, and building hybrid decompositions techniques that exploit this information;2) Tailoring existing decomposition methods for fully interlocked cpPs so that every sub-problem, after backtrack search, has few solutions;and 3) Designing a heuristic for applying local search for fully interlocked cpPs. this work is supported by CAREER Award #0133568 from the National Science Foundation. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Problem solving

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Exploiting multidirectionality in coarse-grained arc consistency algorithms

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9th international conference on principles and practice of constraint programming, cp 2003

作者： Lecoutre, Christophe Boussemart, Frédéric Hemery, Fred CNRS FRE 2499 Rue de l’université SP 16 Lens cedex62307 France

ISBN: (纸本)3540202021

Arc consistency plays a central role in solving constraint Satisfaction Problems. this is the reason why many algorithms have been proposed to establish it. Recently, an algorithm called AC2001 and AC3.1 has been independently presented by their authors. this algorithm which is considered as a refinement of the basic algorithm AC3 has the advantage of being simple and competitive. However, it does not take into account constraint bidirectionality as AC7 does. In this paper, we address this issue, and, in particular, introduce two new algorithms called AC3.2 and AC3.3 which benefit from good properties of both AC3 and AC7. Indeed, AC3.2 and AC3.3 are as easy to implement as AC3 and take advantage of bidirectionality as AC7 does. More precisely, AC3.2 is a general algorithm which partially exploits bidirectionality whereas AC3.3 is a binary algorithm which fully exploits bidirectionality. It turns out that, when Maintaining Arc Consistency during search, MAC3.2, due to a memorization effect, is more efficient than MAC3.3 both in terms of constraint checks and cpu time. Compared to MAC2001/3.1, our experimental results show that MAC3.2 saves about 50% of constraint checks and, on average, 15% of cpu time. © Springer-Verlag Berlin Heidelberg 2003.

关键词： constraint satisfaction problems

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Conflict directed lazy decomposition

Conflict directed lazy decomposition

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18th international conference on principles and practice of constraint programming, cp 2012

作者： Abío, Ignasi Stuckey, Peter J. Barcelona Spain Department of Computing and Information Systems NICTA Victoria Laboratory University of Melbourne Australia

ISBN: (纸本)9783642335570

Two competing approaches to handling complex constraints in satisfaction and optimization problems using SAT and LCG/SMT technology are: decompose the complex constraint into a set of clauses;or (theory) propagate the complex constraint using a standalone algorithm and explain the propagation. Each approach has its benefits. the decomposition approach is prone to an explosion in size to represent the problem, while the propagation approach may require exponentially more search since it does not have access to intermediate literals for explanation. In this paper we show how we can obtain the best of both worlds by lazily decomposing a complex constraint propagator using conflicts to direct it. If intermediate literals are not helpful for conflicts then it will act like the propagation approach, but if they are helpful it will act like the decomposition approach. Experimental results show that it is never much worse than the better of the decomposition and propagation approaches, and sometimes better than both. © 2012 Springer-Verlag.

关键词： constraint satisfaction problems

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Omissions in constraint Acquisition 26th

Omissions in Constraint Acquisition

引用

26th international conference on principles and practice of constraint programming, cp 2020

作者： Tsouros, Dimosthenis C. Stergiou, Kostas Bessiere, Christian Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece CNRS University of Montpellier Montpellier France

ISBN: (纸本)9783030584740

Interactive constraint acquisition is a special case of query-directed learning, also known as "exact" learning. It is used to assist non-expert users in modeling a constraint problem automatically by posting examples to the user that have to be classified as solutions or non-solutions. One significant issue that has not been addressed in the literature of constraint acquisition is the possible presence of uncertainty in the answers of the users. We address this by introducing Limited Membership Queries, where the user has the option of replying "I don’t know", corresponding to "omissions" in exact learning. We present two algorithms for handling omissions. the first one deals with omissions that are independent events, while the second assumes that omissions are related to gaps in the user’s knowledge. We present theoretical results about both methods and we evaluate them on benchmark problems. Importantly, our second algorithm can not only learn (a part of) the target network, but also the constraints that cause the user’s uncertainty. © 2020, Springer Nature Switzerland AG.

关键词： Mergers and acquisitions

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A hybrid paradigm for adaptive parallel search

A hybrid paradigm for adaptive parallel search

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18th international conference on principles and practice of constraint programming, cp 2012

作者： Yun, Xi Epstein, Susan L. Department of Computer Science Graduate Center City University of New York New York NY 10016 United States Department of Computer Science Hunter College City University of New York New York NY 10065 United States

ISBN: (纸本)9783642335570

Parallelization offers the opportunity to accelerate search on constraint satisfaction problems. To parallelize a sequential solver under a popular message passing protocol, the new paradigm described here combines portfolio-based methods and search space splitting. To split effectively and to balance processor workload, this paradigm adaptively exploits knowledge acquired during search and allocates additional resources to the most difficult parts of a problem. Extensive experiments in a parallel environment show that this paradigm significantly improves the performance of an underlying sequential solver, outperforms more naive approaches to parallelization, and solves many difficult problems left open after recent solver competitions. © 2012 Springer-Verlag.

关键词： constraint satisfaction problems

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A regular language membership constraint for finite sequences of variables

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10th international conference on principles and practice of constraint programming, cp 2004

作者： Pesant, Gilles École Polytechnique de Montréal Montreal Canada Université de Montréal C.P. 6128 succ. Centre-ville MontrealH3C 3J7 Canada

ISBN: (纸本)3540232419

this paper describes a global constraint on a fixed-length sequence of finite-domain variables requiring that the corresponding sequence of values taken by these variables belong to a given regular language, thereby generalizing some other known global constraints. We describe and analyze a filtering algorithm achieving generalized arc consistency for this constraint. Some comparative empirical results are also given. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Artificial intelligence

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