47 Search Results for "Solnon, Christine"

Volume

LIPIcs, Volume 235

28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

CP 2022, July 31 to August 8, 2022, Haifa, Israel

Editors: Christine Solnon

Document
DOI: 10.4230/LIPIcs.CP.2023.28
Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming

Authors: Xiao Peng and Christine Solnon

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
The Benzenoid Generation Problem (BGP) aims at generating all benzenoid molecules that satisfy some given properties. This problem has important applications in chemistry, and Carissan et al (2021) have shown us that Constraint Programming (CP) is well suited for modelling this problem because properties defined by chemists are easy to express by means of constraints. Benzenoids are described by hexagon graphs and a key point for an efficient enumeration of these graphs is to be invariant to rotations and symmetries. In this paper, we introduce canonical codes that uniquely characterise hexagon graphs while being invariant to rotations and symmetries. We show that these codes may be defined by means of constraints. We also introduce a global constraint for ensuring that codes are canonical, and a global constraint for ensuring that a pattern is included in a code. We experimentally compare our new CP model with the CP-based approach of Carissan et al (2021), and we show that it has better scale-up properties.
Cite as

Xiao Peng and Christine Solnon. Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 28:1-28:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{peng_et_al:LIPIcs.CP.2023.28,
  author =	{Peng, Xiao and Solnon, Christine},
  title =	{{Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{28:1--28:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.28},
  URN =		{urn:nbn:de:0030-drops-190650},
  doi =		{10.4230/LIPIcs.CP.2023.28},
  annote =	{Keywords: Benzenoid Generation Problem, Canonical Code, Hexagon Graph}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.CP.2022
LIPIcs, Volume 235, CP 2022, Complete Volume

Authors: Christine Solnon

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
LIPIcs, Volume 235, CP 2022, Complete Volume
Cite as

28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 1-692, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Proceedings{solnon:LIPIcs.CP.2022,
  title =	{{LIPIcs, Volume 235, CP 2022, Complete Volume}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{1--692},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022},
  URN =		{urn:nbn:de:0030-drops-166285},
  doi =		{10.4230/LIPIcs.CP.2022},
  annote =	{Keywords: LIPIcs, Volume 235, CP 2022, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.CP.2022.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Christine Solnon

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 0:i-0:xviii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{solnon:LIPIcs.CP.2022.0,
  author =	{Solnon, Christine},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{0:i--0:xviii},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.0},
  URN =		{urn:nbn:de:0030-drops-166292},
  doi =		{10.4230/LIPIcs.CP.2022.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.CP.2022.1
All Questions Answered (Invited Talk)

Authors: Donald E. Knuth

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
During the past two years, the speaker has been drafting Section 7.2.2.3 of "The Art of Computer Programming", which is intended to be a solid introduction to techniques for solving Constraint Satisfaction Problems. The CP 2022 conference is an excellent opportunity for him to get feedback from the leading experts on the subject, and so he was delighted to learn that the organizers were also interested in hearing a few words from him. Rather than giving a canned lecture, he much prefers to let the audience choose the topics, and for all questions to be kept a secret from him until the lecture is actually in progress. (He believes that people often learn more from answers that are spontaneously fumbled than from responses that are carefully preplanned.) Questions related to constraints will naturally be quite welcome, but questions on any subject whatsoever will not be ducked! He'll try to answer them all as best he can, without spending a great deal of time on any one topic, unless there is special interest to go into more depth. Meanwhile he hopes to have drafted some notes for circulation before the conference begins, in case some attendees might wish to focus some of their questions on expository material related to his forthcoming book, either during this session or informally afterwards. Warning: His least favorite questions have the form "What is your favorite X?" If you want to ask such questions, please try to do it cleverly so that he doesn't have to choose between different things that he loves in different ways.
Cite as

Donald E. Knuth. All Questions Answered (Invited Talk). In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{knuth:LIPIcs.CP.2022.1,
  author =	{Knuth, Donald E.},
  title =	{{All Questions Answered}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{1:1--1:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.1},
  URN =		{urn:nbn:de:0030-drops-166305},
  doi =		{10.4230/LIPIcs.CP.2022.1},
  annote =	{Keywords: The Art of Computer Programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.2
Fixed-Template Promise Model Checking Problems

Authors: Kristina Asimi, Libor Barto, and Silvia Butti

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
The fixed-template constraint satisfaction problem (CSP) can be seen as the problem of deciding whether a given primitive positive first-order sentence is true in a fixed structure (also called model). We study a class of problems that generalizes the CSP simultaneously in two directions: we fix a set ℒ of quantifiers and Boolean connectives, and we specify two versions of each constraint, one strong and one weak. Given a sentence which only uses symbols from ℒ, the task is to distinguish whether the sentence is true in the strong sense, or it is false even in the weak sense. We classify the computational complexity of these problems for the existential positive equality-free fragment of first-order logic, i.e., ℒ = {∃,∧,∨}, and we prove some upper and lower bounds for the positive equality-free fragment, ℒ = {∃,∀,∧,∨}. The partial results are sufficient, e.g., for all extensions of the latter fragment.
Cite as

Kristina Asimi, Libor Barto, and Silvia Butti. Fixed-Template Promise Model Checking Problems. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 2:1-2:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{asimi_et_al:LIPIcs.CP.2022.2,
  author =	{Asimi, Kristina and Barto, Libor and Butti, Silvia},
  title =	{{Fixed-Template Promise Model Checking Problems}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{2:1--2:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.2},
  URN =		{urn:nbn:de:0030-drops-166310},
  doi =		{10.4230/LIPIcs.CP.2022.2},
  annote =	{Keywords: Model Checking Problem, First-Order Logic, Promise Constraint Satisfaction Problem, Multi-Homomorphism}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.3
Improved Sample Complexity Bounds for Branch-And-Cut

Authors: Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
The branch-and-cut algorithm for integer programming has a wide variety of tunable parameters that have a huge impact on its performance, but which are challenging to tune by hand. An increasingly popular approach is to use machine learning to configure these parameters based on a training set of integer programs from the application domain. We bound how large the training set should be to ensure that for any configuration, its average performance over the training set is close to its expected future performance. Our guarantees apply to parameters that control the most important aspects of branch-and-cut: node selection, branching constraint selection, and cut selection, and are sharper and more general than those from prior research.
Cite as

Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik. Improved Sample Complexity Bounds for Branch-And-Cut. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{balcan_et_al:LIPIcs.CP.2022.3,
  author =	{Balcan, Maria-Florina and Prasad, Siddharth and Sandholm, Tuomas and Vitercik, Ellen},
  title =	{{Improved Sample Complexity Bounds for Branch-And-Cut}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{3:1--3:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.3},
  URN =		{urn:nbn:de:0030-drops-166321},
  doi =		{10.4230/LIPIcs.CP.2022.3},
  annote =	{Keywords: Automated algorithm configuration, integer programming, machine learning theory, tree search, branch-and-bound, branch-and-cut, cutting planes, sample complexity, generalization guarantees, data-driven algorithm design}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.4
Weisfeiler-Leman Invariant Promise Valued CSPs

Authors: Libor Barto and Silvia Butti

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
In a recent line of work, Butti and Dalmau have shown that a fixed-template Constraint Satisfaction Problem is solvable by a certain natural linear programming relaxation (equivalent to the basic linear programming relaxation) if and only if it is solvable on a certain distributed network, and this happens if and only if its set of Yes instances is closed under Weisfeiler-Leman equivalence. We generalize this result to the much broader framework of fixed-template Promise Valued Constraint Satisfaction Problems. Moreover, we show that two commonly used linear programming relaxations are no longer equivalent in this broader framework.
Cite as

Libor Barto and Silvia Butti. Weisfeiler-Leman Invariant Promise Valued CSPs. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 4:1-4:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{barto_et_al:LIPIcs.CP.2022.4,
  author =	{Barto, Libor and Butti, Silvia},
  title =	{{Weisfeiler-Leman Invariant Promise Valued CSPs}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{4:1--4:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.4},
  URN =		{urn:nbn:de:0030-drops-166332},
  doi =		{10.4230/LIPIcs.CP.2022.4},
  annote =	{Keywords: Promise Valued Constraint Satisfaction Problem, Linear programming relaxation, Distributed algorithms, Symmetric fractional polymorphisms, Color refinement algorithm}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.5
Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data

Authors: Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, and T. K. Satish Kumar

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and runs in near real-time. Second, although automation has improved in maritime operations, current vessel traffic systems (in our case study of a busy Asian port) predict only a single future trajectory of a vessel based on linear extrapolation. Therefore, using historical data we learn a generative model that predicts multiple possible future trajectories of each vessel in a given traffic hotspot, reflecting past vessel movement patterns, and integrate it with our trajectory optimizer. Third, we validate our trajectory optimization and generative model extensively using a real world maritime traffic dataset containing 6 million Automated Identification System (AIS) data records detailing vessel movements over 1.5 years from one of the world’s busiest ports, demonstrating effective risk reduction.
Cite as

Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, and T. K. Satish Kumar. Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 5:1-5:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{basrur_et_al:LIPIcs.CP.2022.5,
  author =	{Basrur, Chaithanya and Singh, Arambam James and Sinha, Arunesh and Kumar, Akshat and Kumar, T. K. Satish},
  title =	{{Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{5:1--5:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.5},
  URN =		{urn:nbn:de:0030-drops-166341},
  doi =		{10.4230/LIPIcs.CP.2022.5},
  annote =	{Keywords: Multi-Agent Path Coordination, Maritime Traffic Control}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.6
Acquiring Maps of Interrelated Conjectures on Sharp Bounds

Authors: Nicolas Beldiceanu, Jovial Cheukam-Ngouonou, Rémi Douence, Ramiz Gindullin, and Claude-Guy Quimper

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
To automate the discovery of conjectures on combinatorial objects, we introduce the concept of a map of sharp bounds on characteristics of combinatorial objects, that provides a set of interrelated sharp bounds for these combinatorial objects. We then describe a Bound Seeker, a CP-based system, that gradually acquires maps of conjectures. The system was tested for searching conjectures on bounds on characteristics of digraphs: it constructs sixteen maps involving 431 conjectures on sharp lower and upper-bounds on eight digraph characteristics.
Cite as

Nicolas Beldiceanu, Jovial Cheukam-Ngouonou, Rémi Douence, Ramiz Gindullin, and Claude-Guy Quimper. Acquiring Maps of Interrelated Conjectures on Sharp Bounds. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{beldiceanu_et_al:LIPIcs.CP.2022.6,
  author =	{Beldiceanu, Nicolas and Cheukam-Ngouonou, Jovial and Douence, R\'{e}mi and Gindullin, Ramiz and Quimper, Claude-Guy},
  title =	{{Acquiring Maps of Interrelated Conjectures on Sharp Bounds}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{6:1--6:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.6},
  URN =		{urn:nbn:de:0030-drops-166353},
  doi =		{10.4230/LIPIcs.CP.2022.6},
  annote =	{Keywords: Acquisition of conjectures, digraphs, bounds}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.7
Parallel Hybrid Best-First Search

Authors: Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
While processor frequency has stagnated over the past two decades, the number of available cores in servers or clusters is still growing, offering the opportunity for significant speed-up in combinatorial optimization. Parallelization of exact methods remains a difficult challenge. We revisit the concept of parallel Branch-and-Bound in the framework of Cost Function Networks. We show how to adapt the anytime Hybrid Best-First Search algorithm in a Master-Worker protocol. The resulting parallel algorithm achieves good load-balancing without introducing new parameters to be tuned as is the case, for example, in Embarrassingly Parallel Search (EPS). It has also a small overhead due to its light communication messages. We performed an experimental evaluation on several benchmarks, comparing our parallel algorithm to its sequential version. We observed linear speed-up in some cases. Our approach compared favourably to the EPS approach and also to a state-of-the-art parallel exact integer programming solver.
Cite as

Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry. Parallel Hybrid Best-First Search. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 7:1-7:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{beldjilali_et_al:LIPIcs.CP.2022.7,
  author =	{Beldjilali, Abdelkader and Montalbano, Pierre and Allouche, David and Katsirelos, George and de Givry, Simon},
  title =	{{Parallel Hybrid Best-First Search}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{7:1--7:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.7},
  URN =		{urn:nbn:de:0030-drops-166362},
  doi =		{10.4230/LIPIcs.CP.2022.7},
  annote =	{Keywords: Combinatorial Optimization, Parallel Branch-and-Bound, CFN}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.8
Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation

Authors: Senne Berden, Mohit Kumar, Samuel Kolb, and Tias Guns

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Many real-world problems can be effectively solved by means of combinatorial optimization. However, appropriate models to give to a solver are not always available, and sometimes must be learned from historical data. Although some research has been done in this area, the task of learning (weighted partial) MAX-SAT models has not received much attention thus far, even though such models can be used in many real-world applications. Furthermore, most existing work is limited to learning models from non-contextual data, where instances are labeled as solutions and non-solutions, but without any specification of the contexts in which those labels apply. A recent approach named hassle-sls has addressed these limitations: it can jointly learn hard constraints and weighted soft constraints from labeled contextual examples. However, it is hindered by long runtimes, as evaluating even a single candidate MAX-SAT model requires solving as many models as there are contexts in the training data, which quickly becomes highly expensive when the size of the model increases. In this work, we address these runtime issues. To this end, we make two contributions. First, we propose a faster model evaluation procedure that makes use of knowledge compilation. Second, we propose a genetic algorithm named hassle-gen that decreases the number of evaluations needed to find good models. We experimentally show that both contributions improve on the state of the art by speeding up learning, which in turn allows higher-quality MAX-SAT models to be found within a given learning time budget.
Cite as

Senne Berden, Mohit Kumar, Samuel Kolb, and Tias Guns. Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{berden_et_al:LIPIcs.CP.2022.8,
  author =	{Berden, Senne and Kumar, Mohit and Kolb, Samuel and Guns, Tias},
  title =	{{Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{8:1--8:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.8},
  URN =		{urn:nbn:de:0030-drops-166373},
  doi =		{10.4230/LIPIcs.CP.2022.8},
  annote =	{Keywords: Machine learning, constraint learning, MAX-SAT}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.9
Complexity of Minimum-Size Arc-Inconsistency Explanations

Authors: Christian Bessiere, Clément Carbonnel, Martin C. Cooper, and Emmanuel Hebrard

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Explaining the outcome of programs has become one of the main concerns in AI research. In constraint programming, a user may want the system to explain why a given variable assignment is not feasible or how it came to the conclusion that the problem does not have any solution. One solution to the latter is to return to the user a sequence of simple reasoning steps that lead to inconsistency. Arc consistency is a well-known form of reasoning that can be understood by a human. We consider explanations as sequences of propagation steps of a constraint on a variable (i.e. the ubiquitous revise function in arc consistency algorithms) that lead to inconsistency. We characterize, on binary CSPs, cases for which providing a shortest such explanation is easy: when domains are Boolean or when variables have maximum degree two. However, these polynomial cases are tight. Providing a shortest explanation is NP-hard if the maximum degree is three, even if the number of variables is bounded, or if domain size is bounded by three. It remains NP-hard on trees, despite the fact that arc consistency is a decision procedure on trees. Finally, the problem is not FPT-approximable unless the Gap-ETH is false.
Cite as

Christian Bessiere, Clément Carbonnel, Martin C. Cooper, and Emmanuel Hebrard. Complexity of Minimum-Size Arc-Inconsistency Explanations. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bessiere_et_al:LIPIcs.CP.2022.9,
  author =	{Bessiere, Christian and Carbonnel, Cl\'{e}ment and Cooper, Martin C. and Hebrard, Emmanuel},
  title =	{{Complexity of Minimum-Size Arc-Inconsistency Explanations}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{9:1--9:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.9},
  URN =		{urn:nbn:de:0030-drops-166380},
  doi =		{10.4230/LIPIcs.CP.2022.9},
  annote =	{Keywords: Constraint programming, constraint propagation, minimum explanations, complexity}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.10
A Constraint Programming Approach to Ship Refit Project Scheduling

Authors: Raphaël Boudreault, Vanessa Simard, Daniel Lafond, and Claude-Guy Quimper

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Ship refit projects require ongoing plan management to adapt to arising work and disruptions. Planners must sequence work activities in the best order possible to complete the project in the shortest time or within a defined period while minimizing overtime costs. Activity scheduling must consider milestones, resource availability constraints, and precedence relations. We propose a constraint programming approach for detailed ship refit planning at two granularity levels, daily and hourly schedule. The problem was modeled using the Cumulative global constraint, and the Solution-Based Phase Saving heuristic was used to speedup the search, thus achieving the industrialization goals. Based on the evaluation of seven realistic instances over three objectives, the heuristic strategy proved to be significantly faster to find better solutions than using a baseline search strategy. The method was integrated into Refit Optimizer, a cloud-based prototype solution that can import projects from Primavera P6 and optimize plans.
Cite as

Raphaël Boudreault, Vanessa Simard, Daniel Lafond, and Claude-Guy Quimper. A Constraint Programming Approach to Ship Refit Project Scheduling. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 10:1-10:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{boudreault_et_al:LIPIcs.CP.2022.10,
  author =	{Boudreault, Rapha\"{e}l and Simard, Vanessa and Lafond, Daniel and Quimper, Claude-Guy},
  title =	{{A Constraint Programming Approach to Ship Refit Project Scheduling}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{10:1--10:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.10},
  URN =		{urn:nbn:de:0030-drops-166396},
  doi =		{10.4230/LIPIcs.CP.2022.10},
  annote =	{Keywords: Ship refit, planning, project management, constraint programming, scheduling, optimization, resource-constrained project scheduling problem}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.11
On Redundancy in Constraint Satisfaction Problems

Authors: Clément Carbonnel

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
A constraint language Γ has non-redundancy f(n) if every instance of CSP(Γ) with n variables contains at most f(n) non-redundant constraints. If Γ has maximum arity r then it has non-redundancy O(n^r), but there are notable examples for which this upper bound is far from the best possible. In general, the non-redundancy of constraint languages is poorly understood and little is known beyond the trivial bounds Ω(n) and O(n^r). In this paper, we introduce an elementary algebraic framework dedicated to the analysis of the non-redundancy of constraint languages. This framework relates redundancy-preserving reductions between constraint languages to closure operators known as pattern partial polymorphisms, which can be interpreted as generic mechanisms to generate redundant constraints in CSP instances. We illustrate the power of this framework by deriving a simple characterisation of all languages of arity r having non-redundancy Θ(n^r).
Cite as

Clément Carbonnel. On Redundancy in Constraint Satisfaction Problems. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 11:1-11:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{carbonnel:LIPIcs.CP.2022.11,
  author =	{Carbonnel, Cl\'{e}ment},
  title =	{{On Redundancy in Constraint Satisfaction Problems}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{11:1--11:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.11},
  URN =		{urn:nbn:de:0030-drops-166409},
  doi =		{10.4230/LIPIcs.CP.2022.11},
  annote =	{Keywords: Constraint satisfaction problem, redundancy, universal algebra, extremal combinatorics}
}
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