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检索条件"任意字段=26th International Conference on Principles and Practice of Constraint Programming, CP 2020"

共 51 条记录，以下是31-40 订阅

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Perturbing Branching Heuristics in constraint Solving 26th

Perturbing Branching Heuristics in Constraint Solving

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

作者： Paparrizou, Anastasia Wattez, Hugues CRIL University of Artois & CNRS Lens France

ISBN: (纸本)9783030584740

Variable ordering heuristics are one of the key settings for an efficient constraint solver. During the last two decades, a considerable effort has been spent for designing dynamic heuristics that iteratively change the order of variables as search progresses. At the same time, restart and randomization methods have been devised for alleviating heavy-tailed phenomena that typically arise in backtrack search. Despite restart methods are now well-understood, choosing how and when to randomize a given heuristic remains an open issue in the design of modern solvers. In this paper, we present several conceptually simple perturbation strategies for incorporating random choices in constraint solving with restarts. the amount of perturbation is controlled and learned in a bandit-driven framework under various stationary and non-stationary exploration policies, during successive restarts. Our experimental evaluation shows significant performance improvements for the perturbed heuristics compared to their classic counterpart, establishing the need for incorporating perturbation in modern constraint solvers. © 2020, Springer Nature Switzerland AG.

关键词： Iterative methods

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Effective Encodings of constraint programming Models to SMT 26th

Effective Encodings of Constraint Programming Models to SMT

引用

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

作者： Davidson, Ewan Akgün, Özgür Espasa, Joan Nightingale, Peter School of Computer Science University of St Andrews St Andrews United Kingdom Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9783030584740

Satisfiability Modulo theories (SMT) is a well-established methodology that generalises propositional satisfiability (SAT) by adding support for a variety of theories such as integer arithmetic and bit-vector operations. SMT solvers have made rapid progress in recent years. In part, the efficiency of modern SMT solvers derives from the use of specialised decision procedures for each theory. In this paper we explore how the Essence Prime constraint modelling language can be translated to the standard SMT-LIB language. We target four theories: bit-vectors (QF_BV), linear integer arithmetic (QF_LIA), non-linear integer arithmetic (QF_NIA), and integer difference logic (QF_IDL). the encodings are implemented in the constraint modelling tool Savile Row. In an extensive set of experiments, we compare our encodings for the four theories, showing some notable differences and complementary strengths. We also compare our new encodings to the existing work targeting SMT and SAT, and to a well-established learning cp solver. Our two proposed encodings targeting the theory of bit-vectors (QF_BV) both substantially outperform earlier work on encoding to QF_BV on a large and diverse set of problem classes. © 2020, Springer Nature Switzerland AG.

关键词： Modeling languages

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Visualizations to Summarize Search Behavior 26th

Visualizations to Summarize Search Behavior

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

作者： Howell, Ian S. Choueiry, Berthe Y. Yu, Hongfeng Constraint Systems Laboratory University of Nebraska-Lincoln LincolnNE68508 United States Visualization Laboratory University of Nebraska-Lincoln LincolnNE68508 United States

ISBN: (纸本)9783030584740

In this paper, we argue that metrics that assess the performance of backtrack search for solving a constraint Satisfaction Problem should not be visualized and examined only at the end of search, but their evolution should be tracked throughout the search process in order to provide a more complete picture of the behavior of search. We describe a process that organizes search history by automatically recognizing qualitatively significant changes in the metrics that assess search performance. To this end, we introduce a criterion for quantifying change between two time instants and a summarization technique for organizing the history of search at controllable levels of abstraction. We validate our approach in the context of two algorithms for enforcing consistency: one that is activated by a surge of backtracking and the second that modifies the structure of the constraint graph. We also introduce a new visualization for exposing the behavior of variable ordering heuristics and validate its usefulness both as a standalone tool and when displayed alongside search history. © 2020, Springer Nature Switzerland AG.

关键词： Visualization

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Learning constraint programming Models from Data Using Generate-And-Aggregate 28

Learning Constraint Programming Models from Data Using Gener...

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28th international conference on principles and practice of constraint programming, cp 2022

作者： Kumar, Mohit Kolb, Samuel Guns, Tias KU Leuven Belgium

ISBN: (纸本)9783959772402

constraint programming (cp) is used widely for solving real-world problems. However, designing these models require substantial expertise. In this paper, we tackle this problem by synthesizing models automatically from past solutions. We introduce COUNT-cp, which uses simple grammars and a generate-and-aggregate approach to learn expressive first-order constraints typically used in cp as well as their parameters from data. the learned constraints generalize across instances over different sizes and can be used to solve unseen instances – e.g., learning constraints from a 4 × 4 Sudoku to solve a 9 × 9 Sudoku or learning nurse staffing requirements across hospitals. COUNT-cp is implemented using the cpMpy constraint programming and modelling environment to produce constraints with nested mathematical expressions. the method is empirically evaluated on a set of suitable benchmark problems and shows to learn accurate and compact models quickly. © Mohit Kumar, Samuel Kolb, and Tias Guns.

关键词： constraint programming

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Dead-End Elimination for Weighted CSP

Dead-End Elimination for Weighted CSP

引用

19th Annual international conference on principles and practice of constraint programming (cp)

作者： de Givry, Simon Prestwich, Steven D. O'Sullivan, Barry INRA UR 875 MIA T F-31320 Castanet Tolosan France Univ Coll Cork Cork Constraint Computat Ctr Cork Ireland

ISBN: (纸本)9783642406263;9783642406270

Soft neighborhood substitutability (SNS) is a powerful technique to automatically detect and prune dominated solutions in combinatorial optimization. Recently, it has been shown in [26] that enforcing partial SNS (PSNSr) during search can be worthwhile in the context of Weighted constraint Satisfaction Problems (WCSP). However, for some problems, especially with large domains, PSNSr is still too costly to enforce due to its worst-case time complexity in O(ned(4)) for binary WCSP. We present a simplified dominance breaking constraint, called restricted dead-end elimination (DEEr), the worst-case time complexity of which is in O(ned(2)). Dead-end elimination was introduced in the context of computational biology as a preprocessing technique to reduce the search space [13, 14, 16, 17, 28, 30]. Our restriction involves testing only one pair of values per variable instead of all the pairs, with the possibility to prune several values at the same time. We further improve the original dead-end elimination criterion, keeping the same time and space complexity as DEEr. Our results show that maintaining DEEr during a depth-first branch and bound (DFBB) search is often faster than maintaining PSNSr and always faster than or similar to DFBB alone.

关键词： combinatorial optimization dominance rule weighted constraint satisfaction problem soft neighborhood substitutability

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Completable partial solutions in constraint programming and constraint-based scheduling

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10th international conference on the principles and practice of constraint programming

作者： Kovács, A Váncza, J Budapest University of Technology and Economics Magyar tudósok körútja 2/d Budapest1117 Hungary Computer and Automation Research Institute Kende utca 13-17 Budapest1111 Hungary

ISBN: (纸本)3540232419

the paper introduces the notion of freely completable partial solutions to characterize constraint satisfaction problems that have components which are relatively easy to solve and are only loosely connected to the remaining parts of the problem. Discovering such partial solutions during the solution process can result in strongly pruned search trees. We give a general definition of freely completable partial solutions, and then apply it to resource-constrained project scheduling. In this domain, we suggest a heuristic algorithm that is able to construct freely completable partial schedules. the method – together with symmetry breaking applied before search – has been successfully tested on real-life resource-constrained project scheduling problems containing up to 2000 tasks.

关键词： constraint satisfaction problems

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Mutational Fuzz Testing for constraint Modeling Systems 30

Mutational Fuzz Testing for Constraint Modeling Systems

引用

30th international conference on principles and practice of constraint programming, cp 2024

作者： Vanroose, Wout Bleukx, Ignace Devriendt, Jo Tsouros, Dimos Verhaeghe, Hélène Guns, Tias DTAI KU Leuven Belgium

ISBN: (纸本)9783959773362

constraint programming (cp) modeling languages, like MiniZinc, Essence and cpMpy, play a crucial role in making cp technology accessible to non-experts. Both solver-independent modeling frameworks and solvers themselves are complex pieces of software that can contain bugs, which undermines their usefulness. Mutational fuzz testing is a way to test complex systems by stochastically mutating input and verifying preserved properties of the mutated output. We investigate different mutations and verification methods that can be used on the constraint specifications directly. this includes methods proposed in the context of SMT problem specifications, as well as new methods related to global constraints, optimization, and solution counting/preservation. Our results show that such a fuzz testing approach improves the overall code coverage of a modeling system compared to only unit testing, and is able to find bugs in the whole toolchain, from the modeling language transformations themselves to the underlying solvers. © Wout Vanroose, Ignace Bleukx, Jo Devriendt, Dimos Tsouros, Hélène Verhaeghe, and Tias Guns.

关键词： Model checking

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constraint Modelling with LLMs Using In-Context Learning 30

Constraint Modelling with LLMs Using In-Context Learning

引用

30th international conference on principles and practice of constraint programming, cp 2024

作者： Michailidis, Kostis Tsouros, Dimos Guns, Tias DTAI KU Leuven Belgium

ISBN: (纸本)9783959773362

constraint programming (cp) allows for the modelling and solving of a wide range of combinatorial problems. However, modelling such problems using constraints over decision variables still requires significant expertise, both in conceptual thinking and syntactic use of modelling languages. In this work, we explore the potential of using pre-trained Large Language Models (LLMs) as coding assistants, to transform textual problem descriptions into concrete and executable cp specifications. We present different transformation pipelines with explicit intermediate representations, and we investigate the potential benefit of various retrieval-augmented example selection strategies for in-context learning. We evaluate our approach on 2 datasets from the literature, namely NL4Opt (optimisation) and Logic Grid Puzzles (satisfaction), and a heterogeneous set of exercises from a cp course. the results show that pre-trained LLMs have promising potential for initialising the modelling process, with retrieval-augmented in-context learning significantly enhancing their modelling capabilities. © Kostis Michailidis, Dimos Tsouros, and Tias Guns.

关键词： Modeling languages

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Search heuristics and heavy-tailed behaviour

引用

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

作者： Hulubei, T O'Sullivan, B Natl Univ Ireland Univ Coll Cork Cork Constraint Computat Ctr Dept Comp Sci Cork Ireland

ISBN: (纸本)3540292381

the heavy-tailed phenomenon that characterises the runtime distributions of backtrack search procedures has received considerable attention over the past few years. Some have conjectured that heavy-tailed behaviour is largely due to the characteristics of the algorithm used. Others have conjectured that problem structure is a significant contributor. In this paper we attempt to explore the former hypothesis, namely we study how variable and value ordering heuristics impact the heavy-tailedness of runtime distributions of backtrack search procedures. We demonstrate that heavy-tailed behaviour can be eliminated from particular classes of random problems by carefully selecting the search heuristics, even when using chronological backtrack search. We also show that combinations of good search heuristics can eliminate heavy tails from Quasigroups with Holes of order 10, and give some insights into why this is the case. these results motivate a more detailed analysis of the effects that variable and value ordering can have on heavy-tailedness. We show how combinations of variable and value ordering heuristics can result in a runtime distribution being inherently heavytailed. Specifically, we show that even if we were to use an Oracle to refute insoluble subtrees optimally, for some combinations of heuristics we would still observe heavy-tailed behaviour. Finally, we study the distributions of refutation sizes found using different combinations of heuristics and gain some further insights into what characteristics tend to give rise to heavy-tailed behaviour.

关键词： Heuristic methods

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Understanding How People Approach constraint Modelling and Solving 28

Understanding How People Approach Constraint Modelling and S...

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28th international conference on principles and practice of constraint programming, cp 2022

作者： Hoffmann, Ruth Zhu, Xu Akgün, Özgür Nacenta, Miguel A. School of Computer Science University of St Andrews United Kingdom Department of Computer Science University of Victoria Canada

ISBN: (纸本)9783959772402

Research in constraint programming typically focuses on problem solving efficiency. However, the way users conceptualise problems and communicate with constraint programming tools is often sidelined. How humans think about constraint problems can be important for the development of efficient tools that are useful to a broader audience. For example, a system incorporating knowledge on how people think about constraint problems can provide explanations to users and improve the communication between the human and the solver. We present an initial step towards a better understanding of the human side of the constraint solving process. To our knowledge, this is the first human-centred study addressing how people approach constraint modelling and solving. We observed three sets of ten users each (constraint programmers, computer scientists and non-computer scientists) and analysed how they find solutions for well-known constraint problems. We found regularities offering clues about how to design systems that are more intelligible to humans. © Ruth Hoffmann, Xu Zhu, Özgür Akgün, and Miguel A. Nacenta.

关键词： constraint theory

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