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Documents authored by Pesant, Gilles

Document
DOI: 10.4230/LIPIcs.CP.2026.18
Neurosymbolic Large Neighbourhood Search

Authors: Arnaud Delage-Reid, Gilles Pesant, and Amal Zouaq

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract
Recent advances in ai have spurred interest in NeSy architectures that integrate neural and symbolic methods. In particular, combining a Constraint Programming (cp) model with a language model for constrained sequence generation tasks allows the neural component to capture domain knowledge while cp enforces structural constraints. In this paper we propose combining cp with a Masked Language Model (mlm) to perform Large Neighbourhood Search (lns). Unlike conventional left-to-right Large Language Models, mlm s can complete sequences with gaps in arbitrary positions, making them well-suited for this task. Meanwhile, lns provides a cp-based iterative framework to explore constrained subspaces whenever searching the whole space would be intractable. We evaluate NeSylns on tasks in constrained text generation and molecule discovery. Our experiments show that it can quickly generate many high-quality sentences and molecules, even for highly-constrained tasks.
Cite as

Arnaud Delage-Reid, Gilles Pesant, and Amal Zouaq. Neurosymbolic Large Neighbourhood Search. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 18:1-18:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{delagereid_et_al:LIPIcs.CP.2026.18,
  author =	{Delage-Reid, Arnaud and Pesant, Gilles and Zouaq, Amal},
  title =	{{Neurosymbolic Large Neighbourhood Search}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{18:1--18:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.18},
  URN =		{urn:nbn:de:0030-drops-266514},
  doi =		{10.4230/LIPIcs.CP.2026.18},
  annote =	{Keywords: Large Neighbourhood Search, Neurosymbolic AI, Constraint Programming, Masked Language Model, CPBP Solver}
}
Document
DOI: 10.4230/LIPIcs.CP.2026.55
An Automata-Based Constraint Programming Framework for Optimal Classical Planning

Authors: Damien Van Meerbeeck, Arnaud Lequen, Gilles Pesant, and Jendrik Seipp

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract
Recent work has shown that classical planning tasks can be compactly factored into deterministic finite automata and solved optimally with constraint programming (CP). In this setting, finding a plan reduces to finding a word accepted by all automata through Regular constraints. So far, however, these automata have had to be carefully handcrafted from PDDL tasks. In this paper, we show that they can instead be generated automatically and used as the basis of CP models. We also show that the resulting framework is easily extensible with additional constraints from the planning literature that strengthen propagation. Our approach solves more tasks than the state of the art in end-to-end CP for classical planning in almost all domains.
Cite as

Damien Van Meerbeeck, Arnaud Lequen, Gilles Pesant, and Jendrik Seipp. An Automata-Based Constraint Programming Framework for Optimal Classical Planning. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 55:1-55:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{vanmeerbeeck_et_al:LIPIcs.CP.2026.55,
  author =	{Van Meerbeeck, Damien and Lequen, Arnaud and Pesant, Gilles and Seipp, Jendrik},
  title =	{{An Automata-Based Constraint Programming Framework for Optimal Classical Planning}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{55:1--55:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.55},
  URN =		{urn:nbn:de:0030-drops-266889},
  doi =		{10.4230/LIPIcs.CP.2026.55},
  annote =	{Keywords: Optimal Classical Planning, Landmark Constraints, Automata}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.30
Learning Precedences for Scheduling Problems with Graph Neural Networks

Authors: Hélène Verhaeghe, Quentin Cappart, Gilles Pesant, and Claude-Guy Quimper

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
The resource constrained project scheduling problem (RCPSP) consists of scheduling a finite set of resource-consuming tasks within a temporal horizon subject to resource capacities and precedence relations between pairs of tasks. It is NP-hard and many techniques have been introduced to improve the efficiency of CP solvers to solve it. The problem is naturally represented as a directed graph, commonly referred to as the precedence graph, by linking pairs of tasks subject to a precedence. In this paper, we propose to leverage the ability of graph neural networks to extract knowledge from precedence graphs. This is carried out by learning new precedences that can be used either to add new constraints or to design a dedicated variable-selection heuristic. Experiments carried out on RCPSP instances from PSPLIB show the potential of learning to predict precedences and how they can help speed up the search for solutions by a CP solver.
Cite as

Hélène Verhaeghe, Quentin Cappart, Gilles Pesant, and Claude-Guy Quimper. Learning Precedences for Scheduling Problems with Graph Neural Networks. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 30:1-30:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{verhaeghe_et_al:LIPIcs.CP.2024.30,
  author =	{Verhaeghe, H\'{e}l\`{e}ne and Cappart, Quentin and Pesant, Gilles and Quimper, Claude-Guy},
  title =	{{Learning Precedences for Scheduling Problems with Graph Neural Networks}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{30:1--30:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.30},
  URN =		{urn:nbn:de:0030-drops-207150},
  doi =		{10.4230/LIPIcs.CP.2024.30},
  annote =	{Keywords: Scheduling, Precedence graph, Graph neural network}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.6
Optimization of Short-Term Underground Mine Planning Using Constraint Programming

Authors: Younes Aalian, Gilles Pesant, and Michel Gamache

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

Abstract
Short-term underground mine planning problems are often difficult to solve due to the large number of activities and diverse machine types to be scheduled, as well as multiple operational constraints. This paper presents a Constraint Programming (CP) model to optimize short-term scheduling for the Meliadine underground gold mine in Nunavut, Canada, taking into consideration operational constraints and the daily development and production targets of the mine plan. To evaluate the efficacy of the developed CP short-term planning model, we compare schedules generated by the CP model with the ones created manually by the mine planner for two real data sets. Results demonstrate that the CP model outperforms the manual approach by generating more efficient schedules with lower makespans.
Cite as

Younes Aalian, Gilles Pesant, and Michel Gamache. Optimization of Short-Term Underground Mine Planning Using Constraint Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{aalian_et_al:LIPIcs.CP.2023.6,
  author =	{Aalian, Younes and Pesant, Gilles and Gamache, Michel},
  title =	{{Optimization of Short-Term Underground Mine Planning Using Constraint Programming}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{6:1--6:16},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.6},
  URN =		{urn:nbn:de:0030-drops-190430},
  doi =		{10.4230/LIPIcs.CP.2023.6},
  annote =	{Keywords: Mine planning, Constraint Programming, Short-term planning, Underground mine, Scheduling}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.30
Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints

Authors: Daphné Lafleur, Sarath Chandar, and Gilles Pesant

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

Abstract
While Machine Learning (ML) techniques are good at generating data similar to a dataset, they lack the capacity to enforce constraints. On the other hand, any solution to a Constraint Programming (CP) model satisfies its constraints but has no obligation to imitate a dataset. Yet, we sometimes need both. In this paper we borrow RL-Tuner, a Reinforcement Learning (RL) algorithm introduced to tune neural networks, as our enabling architecture to exploit the respective strengths of ML and CP. RL-Tuner maximizes the sum of a pretrained network’s learned probabilities and of manually-tuned penalties for each violated constraint. We replace the latter with outputs of a CP model representing the marginal probabilities of each value and the number of constraint violations. As was the case for the original RL-Tuner, we apply our algorithm to music generation since it is a highly-constrained domain for which CP is especially suited. We show that combining ML and CP, as opposed to using them individually, allows the agent to reflect the pretrained network while taking into account constraints, leading to melodic lines that respect both the corpus' style and the music theory constraints.
Cite as

Daphné Lafleur, Sarath Chandar, and Gilles Pesant. Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 30:1-30:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{lafleur_et_al:LIPIcs.CP.2022.30,
  author =	{Lafleur, Daphn\'{e} and Chandar, Sarath and Pesant, Gilles},
  title =	{{Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{30:1--30:16},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.30},
  URN =		{urn:nbn:de:0030-drops-166594},
  doi =		{10.4230/LIPIcs.CP.2022.30},
  annote =	{Keywords: Constraint programming, reinforcement learning, RNN, music generation}
}
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