10 Search Results for "Beldiceanu, Nicolas"

Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2025.42
Towards Modern and Modular SAT for LCG (Short Paper)

Authors: Jip J. Dekker, Alexey Ignatiev, Peter J. Stuckey, and Allen Z. Zhong

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Lazy Clause Generation (LCG) is an architecture for building Constraint Programming (CP) solvers using an underlying Boolean Satisfiability (SAT) engine. The CP propagation engine lazily creates clauses that define the integer variables and impose problem restrictions. The SAT engine uses the clausal model to reason and search, including, crucially, the generation of nogoods. However, while SAT solving has made significant advances recently, the underlying SAT technology in most LCG solvers has largely remained the same. Using a new interface to SAT engines, IPASIR-UP, we can construct an LCG solver which can swap out the underlying SAT engine with any that supports the interface. This new approach means we need to revisit many of the design and engineering decisions for LCG solvers, to take maximum advantage of a better underlying SAT engine while adhering to the restrictions of the interface. In this paper, we explore the possibilities and challenges of using IPASIR-UP for LCG, showing that it can be used to create a highly competitive solver.
Cite as

Jip J. Dekker, Alexey Ignatiev, Peter J. Stuckey, and Allen Z. Zhong. Towards Modern and Modular SAT for LCG (Short Paper). In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 42:1-42:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dekker_et_al:LIPIcs.CP.2025.42,
  author =	{Dekker, Jip J. and Ignatiev, Alexey and Stuckey, Peter J. and Zhong, Allen Z.},
  title =	{{Towards Modern and Modular SAT for LCG}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{42:1--42:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.42},
  URN =		{urn:nbn:de:0030-drops-239038},
  doi =		{10.4230/LIPIcs.CP.2025.42},
  annote =	{Keywords: Lazy Clause Generation, Boolean Satisfiability, IPASIR-UP}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2025.43
Scheduling Telescope Observations for the European Southern Observatory (Short Paper)

Authors: Michael Prümm, Peter Nightingale, and Felix Ulrich-Oltean

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
The European Southern Observatory (ESO) provides state-of-the-art large telescope facilities at three sites in Chile, supported by 16 European member states. Astronomers submit proposals for sets of observations which are reviewed and ranked based on scientific merit, then a schedule is constructed respecting the ranking and aiming to make the fullest use of the various telescopes and numerous instruments. Currently a schedule covers six months, but in the near future ESO will switch to annual schedules. Here we examine the most challenging scheduling problem encountered by ESO: scheduling the operations of the Very Large Telescope Interferometer (VLTI) on Paranal, Chile. Tasks to be scheduled include observations performed by ESO staff, "visitor mode" periods where astronomers visit the site to use the telescopes, various maintenance tasks, and reconfiguration tasks taking multiple days. Typically a VLTI six-month schedule would contain approximately 450 activities. We explore global constraint models and a SAT encoding of the problem.
Cite as

Michael Prümm, Peter Nightingale, and Felix Ulrich-Oltean. Scheduling Telescope Observations for the European Southern Observatory (Short Paper). In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 43:1-43:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{prumm_et_al:LIPIcs.CP.2025.43,
  author =	{Pr\"{u}mm, Michael and Nightingale, Peter and Ulrich-Oltean, Felix},
  title =	{{Scheduling Telescope Observations for the European Southern Observatory}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{43:1--43:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.43},
  URN =		{urn:nbn:de:0030-drops-239041},
  doi =		{10.4230/LIPIcs.CP.2025.43},
  annote =	{Keywords: Modelling, Constraint Programming, Scheduling, SAT, Global Constraints}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.35
Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems

Authors: Konstantin Sidorov, Imko Marijnissen, and Emir Demirović

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Constraint programming solvers have seen much success in scheduling problems owing to their efficient reasoning over constraints to solve complex problems in practice. Many algorithms have been proposed for propagating information from a single constraint. However, inferring and exchanging information across multiple constraints can provide deeper insight into the global structure of a problem. In this work, we propose to exchange information amongst constraints by inferring the disjointness of tasks in scheduling problems from many constraints. We do this by (i) augmenting existing propagators, such as the Cumulative and nogoods, to report when pairs of tasks are disjoint, and (ii) leveraging this information by introducing the SelectiveDisjunctive propagator which generates a lower bound on the earliest completion time of cliques of disjoint tasks to determine conflicts. This allows us to aggregate disjointness information spanning multiple constraints to gain a better global overview of the problem, as well as more precise local information. We also identify a problem structure where an LCG solver reasoning over Cumulative constraints separately, without any reformulations, requires an exponential amount of time to prove infeasibility, which we both justify theoretically and show empirically; on the other hand, our approach solves those instances in polynomial time. On particular known RCPSP and RCPSP/max benchmarks, our approach significantly reduces the number of conflicts required to prove optimality when resource contention is high. Additionally, we discover new lower bounds for 16 RCPSP/max instances (closing six of them) and four RCPSP instances (closing one), as well as new upper bounds for two RCPSP/max instances and four RCPSP instances. Furthermore, we empirically analyse our proposed approach to determine which features are beneficial for performance, showing that finding cliques is one of the main bottlenecks and that detecting disjointness during search can lead to improved bounds on certain instances, but it generally negatively impacts learning. This work paves the way for reasoning over the disjointness of tasks inferred from a variety of standard constraints to discover novel information sourced from multiple constraints during search.
Cite as

Konstantin Sidorov, Imko Marijnissen, and Emir Demirović. Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 35:1-35:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sidorov_et_al:LIPIcs.CP.2025.35,
  author =	{Sidorov, Konstantin and Marijnissen, Imko and Demirovi\'{c}, Emir},
  title =	{{Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{35:1--35:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.35},
  URN =		{urn:nbn:de:0030-drops-238969},
  doi =		{10.4230/LIPIcs.CP.2025.35},
  annote =	{Keywords: Constraint Programming, Lazy Clause Generation, Propagation, Scheduling, Cumulative, Disjunctive}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.33
Constraint-Based In-Station Train Dispatching

Authors: Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
In-station dispatching is the problem of planning the movements of scheduled trains inside a railway station. Effective solutions for in-station dispatching are important for maximising the utilisation of railway infrastructure and for mitigating the impact of incidents and delays in the broader network. In this paper, we explore a constraint-based approach to perform in-station train dispatching. Our extensive empirical analysis of multiple modelling, search strategy, and solver choices, performed over synthetically generated, yet realistic, data, shows that our method outperforms the existing planning-based state-of-the-art approach. In addition, we present different optimisation criteria, which can be effortless defined thanks to the constraint-based approach.
Cite as

Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati. Constraint-Based In-Station Train Dispatching. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 33:1-33:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schutt_et_al:LIPIcs.CP.2025.33,
  author =	{Schutt, Andreas and Cardellini, Matteo and Dekker, Jip J. and Harabor, Daniel and Maratea, Marco and Vallati, Mauro},
  title =	{{Constraint-Based In-Station Train Dispatching}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{33:1--33:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.33},
  URN =		{urn:nbn:de:0030-drops-238941},
  doi =		{10.4230/LIPIcs.CP.2025.33},
  annote =	{Keywords: in-station train dispatching, train scheduling, railway scheduling, constraint programming, mixed-integer programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.32
BFS-Based Canonical Codes for Generating Graphs with Constraint Programming

Authors: Xiao Peng and Christine Solnon

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We consider the problem of generating all graphs that satisfy some given additional constraints (on vertex degrees, or cycle lengths, for example). Most previous works have proposed to generate canonical codes associated with adjacency matrices. In this paper, we consider canonical codes based on Breadth First Search (BFS), and we show how to generate them with Constraint Programming (CP): we introduce a set of basic constraints that must be satisfied by all canonical codes, thus breaking many symmetries, and we introduce a global constraint to break other symmetries. We illustrate the interest of our approach on connected claw-free cubic graphs, and show that it outperforms state-of-the-art CP and SAT Modulo Theory (SMT) approaches.
Cite as

Xiao Peng and Christine Solnon. BFS-Based Canonical Codes for Generating Graphs with Constraint Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 32:1-32:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{peng_et_al:LIPIcs.CP.2025.32,
  author =	{Peng, Xiao and Solnon, Christine},
  title =	{{BFS-Based Canonical Codes for Generating Graphs with Constraint Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{32:1--32:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.32},
  URN =		{urn:nbn:de:0030-drops-238935},
  doi =		{10.4230/LIPIcs.CP.2025.32},
  annote =	{Keywords: Graph Generation, Automorphisms, Symmetry Breaking}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.7
Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability

Authors: Manuel Chastenay, Xavier Zwingmann, Claude-Guy Quimper, and Jonathan Gaudreault

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
In industrial settings, cutting predefined pieces from one or multiple sheets of material is a common optimization challenge. This problem can be formulated as a variant of the 2D bin packing problem, where the edges of the pieces define the cut lines. This paper presents a constraint programming model developed in collaboration with an industrial partner in construction to minimize scrap waste generated when cutting insulation pieces. The model introduces an objective function designed to maximize the reusability of leftover material. To fully leverage the model’s efficiency, an initial process transforms irregular insulation pieces into rectangles using one of four processing methods. A comparative analysis is conducted to evaluate the impact of these methods, as well as to benchmark the model’s results against the partner’s manual approach.
Cite as

Manuel Chastenay, Xavier Zwingmann, Claude-Guy Quimper, and Jonathan Gaudreault. Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 7:1-7:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chastenay_et_al:LIPIcs.CP.2025.7,
  author =	{Chastenay, Manuel and Zwingmann, Xavier and Quimper, Claude-Guy and Gaudreault, Jonathan},
  title =	{{Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{7:1--7:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.7},
  URN =		{urn:nbn:de:0030-drops-238685},
  doi =		{10.4230/LIPIcs.CP.2025.7},
  annote =	{Keywords: Combinatorial optimization, constraint programming, 2D bin packing}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.27
Understanding the Impact of Value Selection Heuristics in Scheduling Problems

Authors: Tim Luchterhand, Emmanuel Hebrard, and Sylvie Thiébaux

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
It has been observed that value selection heuristics have less impact than other heuristic choices when solving hard combinatorial optimization (CO) problems. It is often thought that this is because more time is spent on unsatisfiable sub-problems where the value ordering is irrelevant. In this paper we investigate this belief in the scheduling domain and come up with a more detailed explanation. We find that, even though there are less relevant choices to be made on hard instances, each mistake tends to have a bigger impact, to a point where the potential gain from a value heuristic predominates. Moreover, we observe two interesting and relatively surprising phenomena when solving scheduling problems. First, the accuracy of a given value selection heuristic decreases with the optimality gap. Second, the computational penalty of a mistake increases with the accuracy of the heuristic. For the first observation, we argue that on hard problems, constraint propagation removes a large portion of choices that align with the intuition behind the heuristic. This means that the heuristic faces mostly difficult choices. For the second observation, we argue that simple heuristics tend to make more mistakes on intuitive choice points, and the computational cost for refuting these mistakes is smaller than for those made by a more accurate heuristic.
Cite as

Tim Luchterhand, Emmanuel Hebrard, and Sylvie Thiébaux. Understanding the Impact of Value Selection Heuristics in Scheduling Problems. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{luchterhand_et_al:LIPIcs.CP.2025.27,
  author =	{Luchterhand, Tim and Hebrard, Emmanuel and Thi\'{e}baux, Sylvie},
  title =	{{Understanding the Impact of Value Selection Heuristics in Scheduling Problems}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{27:1--27:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.27},
  URN =		{urn:nbn:de:0030-drops-238885},
  doi =		{10.4230/LIPIcs.CP.2025.27},
  annote =	{Keywords: Scheduling, Branching Heuristics, Constraint Programming}
}
Document
DOI: 10.4230/OASIcs.SLATE.2025.8
An Architecture for Composite Combinatorial Optimization Solvers

Authors: Khalil Chrit, Jean-François Baffier, Pedro Patinho, and Salvador Abreu

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract
In this paper, we introduce elements for MoSCO, a framework for building hybrid metaheuristic-based solvers from a collection of reusable base components. The framework is implemented in Julia and provides a modular architecture for composing solvers through a pipeline-based approach. The modular design of MoSCO supports the creation of reusable components and adaptable solver strategies for various Constraint Satisfaction Problems (CSPs) and Constraint Optimization Problems (COPs). We validate MoSCO’s utility through practical examples, demonstrating its effectiveness in reconstructing established metaheuristics and enabling the creation of novel solver configurations. This work lays the foundation for future developments in automated solver construction and parameter optimization.
Cite as

Khalil Chrit, Jean-François Baffier, Pedro Patinho, and Salvador Abreu. An Architecture for Composite Combinatorial Optimization Solvers. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chrit_et_al:OASIcs.SLATE.2025.8,
  author =	{Chrit, Khalil and Baffier, Jean-Fran\c{c}ois and Patinho, Pedro and Abreu, Salvador},
  title =	{{An Architecture for Composite Combinatorial Optimization Solvers}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{8:1--8:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.8},
  URN =		{urn:nbn:de:0030-drops-236885},
  doi =		{10.4230/OASIcs.SLATE.2025.8},
  annote =	{Keywords: Hybrid Metaheuristics, DSL}
}
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.
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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.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/OASIcs.Gabbrielli.11
Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Authors: Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract
This paper shows how we can combine the power of machine learning with the flexibility of constraints. More specifically, we show how machine learning models can be represented by first-order logic theories, and how to derive these theories. The advantage of this representation is that it can be augmented with additional formulae, representing constraints of some kind on the data domain. For instance, new knowledge, or potential attackers, or fairness desiderata. We consider various kinds of learning algorithms (neural networks, k-nearest-neighbours, decision trees, support vector machines) and for each of them we show how to infer the FOL formulae. Then we focus on one particular application domain, namely the field of security and privacy. The idea is to represent the potentialities and goals of the attacker as a set of constraints, then use a constraint solver (more precisely, a solver modulo theories) to verify the satisfiability. If a solution exists, then it means that an attack is possible, otherwise, the system is safe. We show various examples from different areas of security and privacy; specifically, we consider a side-channel attack on a password checker, a malware attack on smart health systems, and a model-inversion attack on a neural network.
Cite as

Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli. Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{falaschi_et_al:OASIcs.Gabbrielli.11,
  author =	{Falaschi, Moreno and Palamidessi, Catuscia and Romanelli, Marco},
  title =	{{Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{11:1--11:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.11},
  URN =		{urn:nbn:de:0030-drops-132338},
  doi =		{10.4230/OASIcs.Gabbrielli.11},
  annote =	{Keywords: Constraints, machine learning, privacy, security}
}
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