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Bart Bogaerts

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30 papers
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30

AAAI Conference 2026 Conference Paper

Certified Branch-and-Bound MaxSAT Solving

  • Dieter Vandesande
  • Jordi Coll
  • Bart Bogaerts

Over the past few decades, combinatorial solvers have seen remarkable performance improvements, enabling their practical use in real-world applications. In some of these applications, ensuring the correctness of the solver's output is critical. However, the complexity of modern solvers makes them susceptible to bugs in their source code. In the domain of satisfiability checking (SAT), this issue has been addressed through proof logging, where the solver generates a formal proof of the correctness of its answer. For more expressive problems like MaxSAT, the optimization variant of SAT, proof logging had not seen a comparable breakthrough until recently. In this paper, we show how to achieve proof logging for state-of-the-art techniques in Branch-and-Bound MaxSAT solving. This includes certifying look-ahead methods used in such algorithms as well as advanced clausal encodings of pseudo-Boolean constraints based on so-called Multi-Valued Decision Diagrams (MDDs). We implement these ideas in MaxCDCL, the dominant branch-and-bound solver, and experimentally demonstrate that proof logging is feasible with limited overhead, while proof checking remains a challenge.

AAAI Conference 2026 Conference Paper

Efficient and Reliable Hitting-Set Computations for the Implicit Hitting Set Approach

  • Hannes Ihalainen
  • Dieter Vandesande
  • André Schidler
  • Jeremias Berg
  • Bart Bogaerts
  • Matti Järvisalo

The implicit hitting set (IHS) approach offers a general framework for solving computationally hard combinatorial optimization problems declaratively. IHS iterates between a decision oracle used for extracting sources of inconsistency and an optimizer for computing so-called hitting sets (HSs) over the accumulated sources of inconsistency. While the decision oracle is language-specific, the optimizers is usually instantiated through integer programming. We explore alternative algorithmic techniques for hitting set optimization based on different ways of employing pseudo-Boolean (PB) reasoning as well as stochastic local search. We extensively evaluate the practical feasibility of the alternatives in particular in the context of pseudo-Boolean (0-1 IP) optimization as one of the most recent instantiations of IHS. Highlighting a trade-off between efficiency and reliability, while a commercial IP solver turns out to remain the most effective way to instantiate HS computations, it can cause correctness issues due to numerical instability; in fact, we show that exact HS computations instantiated via PB reasoning can be made competitive with a numerically exact IP solver. Furthermore, the use of PB reasoning as a basis for HS computations allows for obtaining certificates for the correctness of IHS computations, generally applicable to any IHS instantiation in which reasoning in the declarative language at hand can be captured in the PB-based proof format we employ.

AAAI Conference 2026 Conference Paper

Faster Certified Symmetry Breaking Using Orders with Auxiliary Variables

  • Markus Anders
  • Bart Bogaerts
  • Benjamin Bogø
  • Arthur Gontier
  • Wietze Koops
  • Ciaran McCreesh
  • Magnus O. Myreen
  • Jakob Nordström

Symmetry breaking is a crucial technique in modern combinatorial solving, but it is difficult to be sure it is implemented correctly. The most successful approach to deal with bugs is to make solvers certifying, so that they output not just a solution, but also a mathematical proof of correctness in a standard format, which can then be checked by a formally verified checker. This requires justifying symmetry reasoning within the proof, but developing efficient methods for this has remained a long-standing open challenge. A fully general approach was recently proposed, but it relies on encoding lexicographic orders with big integers, which quickly becomes infeasible for large symmetries. In this work, we develop a method for instead encoding orders with auxiliary variables. We show that this leads to orders-of-magnitude speed-ups in both theory and practice by running experiments on proof logging and checking for SAT symmetry breaking using the state-of-the-art satsuma symmetry breaker and the VeriPB proof checking toolchain.

AAAI Conference 2026 Conference Paper

Preference Elicitation for Step-Wise Explanations in Logic Puzzles

  • Marco Foschini
  • Marianne Defresne
  • Emilio Gamba
  • Bart Bogaerts
  • Tias Guns

Step-wise explanations can explain logic puzzles and other satisfaction problems by showing how to derive decisions step by step. Each step consists of a set of constraints that derive an assignment to one or more decision variables. However, many candidate explanation steps exist, with different sets of constraints and different decisions they derive. To identify the most comprehensible one, a user-defined objective function is required to quantify the quality of each step. However, defining a good objective function is challenging. Here, interactive preference elicitation methods from the wider machine learning community can offer a way to learn user preferences from pairwise comparisons. We investigate the feasibility of this approach for step-wise explanations and address several limitations that distinguish it from elicitation for standard combinatorial problems. First, because the explanation quality is measured using multiple sub-objectives that can vary a lot in scale, we propose two dynamic normalization techniques to rescale these features and stabilize the learning process. We also observed that many generated comparisons involve similar explanations. For this reason, we introduce MACHOP (Multi-Armed CHOice Perceptron), a novel query generation strategy that integrates non-domination constraints with upper confidence bound-based diversification. We evaluate the elicitation techniques on Sudokus and Logic-Grid puzzles using artificial users, and validate them with a real-user evaluation. In both settings, MACHOP consistently produces higher-quality explanations than the standard approach.

AAAI Conference 2026 Conference Paper

Using Certifying Constraint Solvers for Generating Step-wise Explanations

  • Ignace Bleukx
  • Maarten Flippo
  • Bart Bogaerts
  • Emir Demirović
  • Tias Guns

In the field of Explainable Constraint Solving, it is common to explain to a user why a problem is unsatisfiable. A recently proposed method for this is to compute a sequence of explanation steps. Such a step-wise explanation shows individual reasoning steps involving constraints from the original specification, that in the end explain a conflict. However, computing a step-wise explanation is computationally expensive, limiting the scope of problems for which it can be used. We investigate how we can use proofs generated by a constraint solver as a starting point for computing step-wise explanations, instead of computing them step-by-step. More specifically, we define a framework of abstract proofs, in which \textit{both} proofs and step-wise explanations can be represented. We then propose several methods for converting a proof to a step-wise explanation sequence, with special attention to trimming and simplification techniques to keep the sequence and its individual steps small. Our results show our method significantly speeds up the generation of step-wise explanation sequences, while the resulting step-wise explanation has a quality similar to the current state-of-the-art.

AAAI Conference 2025 Conference Paper

Exploiting Symmetries in MUS Computation

  • Ignace Bleukx
  • Hélène Verhaeghe
  • Bart Bogaerts
  • Tias Guns

In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from a set of unsatisfiable constraints. This helps explain to a user why a constraint specification does not admit a solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations of constraints need to be considered. In the traditional context of solving satisfaction problems, symmetry has been well studied, and effective ways to detect and exploit symmetries during the search exist. However, in the setting of finding MUSes of unsatisfiable constraint programs, symmetries are understudied. In this paper, we take inspiration from existing symmetry-handling techniques and adapt well-known MUS-computation methods to exploit symmetries in the specification, speeding-up overall computation time. Our results display a significant reduction of runtime for our adapted algorithms compared to the baseline on symmetric problems.

AAAI Conference 2024 Conference Paper

Using Symmetries to Lift Satisfiability Checking

  • Pierre Carbonnelle
  • Gottfried Schenner
  • Maurice Bruynooghe
  • Bart Bogaerts
  • Marc Denecker

We analyze how symmetries can be used to compress structures (also known as interpretations) onto a smaller domain without loss of information. This analysis suggests the possibility to solve satisfiability problems in the compressed domain for better performance. Thus, we propose a 2-step novel method: (i) the sentence to be satisfied is automatically translated into an equisatisfiable sentence over a ``lifted'' vocabulary that allows domain compression; (ii) satisfiability of the lifted sentence is checked by growing the (initially unknown) compressed domain until a satisfying structure is found. The key issue is to ensure that this satisfying structure can always be expanded into an uncompressed structure that satisfies the original sentence to be satisfied. We present an adequate translation for sentences in typed first-order logic extended with aggregates. Our experimental evaluation shows large speedups for generative configuration problems. The method also has applications in the verification of software operating on complex data structures. Our results justify further research in automatic translation of sentences for symmetry reduction.

JAIR Journal 2023 Journal Article

Certified Dominance and Symmetry Breaking for Combinatorial Optimisation

  • Bart Bogaerts
  • Stephan Gocht
  • Ciaran McCreesh
  • Jakob Nordström

Symmetry and dominance breaking can be crucial for solving hard combinatorial search and optimisation problems, but the correctness of these techniques sometimes relies on subtle arguments. For this reason, it is desirable to produce efficient, machine-verifiable certificates that solutions have been computed correctly. Building on the cutting planes proof system, we develop a certification method for optimisation problems in which symmetry and dominance breaking is easily expressible. Our experimental evaluation demonstrates that we can efficiently verify fully general symmetry breaking in Boolean satisfiability (SAT) solving, thus providing, for the first time, a unified method to certify a range of advanced SAT techniques that also includes cardinality and parity (XOR) reasoning. In addition, we apply our method to maximum clique solving and constraint programming as a proof of concept that the approach applies to a wider range of combinatorial problems.

JAIR Journal 2023 Journal Article

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

  • Emilio Gamba
  • Bart Bogaerts
  • Tias Guns

We build on a recently proposed method for stepwise explaining the solutions to Constraint Satisfaction Problems (CSPs) in a human understandable way. An explanation here is a sequence of simple inference steps where simplicity is quantified by a cost function. Explanation generation algorithms rely on extracting Minimal Unsatisfiable Subsets (MUSs) of a derived unsatisfiable formula, exploiting a one-to-one correspondence between so-called non-redundant explanations and MUSs. However, MUS extraction algorithms do not guarantee subset minimality or optimality with respect to a given cost function. Therefore, we build on these formal foundations and address the main points of improvement, namely how to generate explanations efficiently that are provably optimal (with respect to the given cost metric). To this end, we developed (1) a hitting set-based algorithm for finding the optimal constrained unsatisfiable subsets; (2) a method for reusing relevant information across multiple algorithm calls; and (3) methods for exploiting domain-specific information to speed up the generation of explanation sequences. We have experimentally validated our algorithms on a large number of CSP problems. We found that our algorithms outperform the MUS approach in terms of explanation quality and computational time (on average up to 56 % faster than a standard MUS approach).

KR Conference 2023 Conference Paper

Mathematical Foundations for Joining Only Knowing and Common Knowledge

  • Marcos Cramer
  • Samuele Pollaci
  • Bart Bogaerts

Common knowledge and only knowing capture two intuitive and natural notions that have proven to be useful in a variety of settings, for example to reason about coordination or agreement between agents, or to analyse the knowledge of knowledge-based agents. While these two epistemic operators have been extensively studied in isolation, the approaches made to encode their complex interplay failed to capture some essential properties of only knowing. We propose a novel solution by defining a notion of μ-biworld for countable ordinals μ, which approximates not only the worlds that an agent deems possible, but also those deemed impossible. This approach allows us to define a multi-agent epistemic logic with common knowledge and only knowing operators, and a three-valued model semantics for it. Moreover, we show that we only really need biworlds of depth at most ω²+1. Based on this observation, we define a Kripke semantics on a canonical Kripke structure and show that this semantics coincides with the model semantics. Finally, we discuss issues arising when combining negative introspection or truthfulness with only knowing and show how positive introspection can be integrated into our logic.

AAAI Conference 2022 Conference Paper

Certified Symmetry and Dominance Breaking for Combinatorial Optimisation

  • Bart Bogaerts
  • Stephan Gocht
  • Ciaran McCreesh
  • Jakob Nordström

Symmetry and dominance breaking can be crucial for solving hard combinatorial search and optimisation problems, but the correctness of these techniques sometimes relies on subtle arguments. For this reason, it is desirable to produce efficient, machine-verifiable certificates that solutions have been computed correctly. Building on the cutting planes proof system, we develop a certification method for optimisation problems in which symmetry and dominance breaking are easily expressible. Our experimental evaluation demonstrates that we can efficiently verify fully general symmetry breaking in Boolean satisfiability (SAT) solving, thus providing, for the first time, a unified method to certify a range of advanced SAT techniques that also includes XOR and cardinality reasoning. In addition, we apply our method to maximum clique solving and constraint programming as a proof of concept that the approach applies to a wider range of combinatorial problems.

IS Journal 2022 Journal Article

Computing Abductive Explanations

  • Luciano Caroprese
  • Ester Zumpano
  • Bart Bogaerts

We study the computation of constrained explanations in the framework of abductive logic programming. A general characteristic of abductive reasoning is the existence of multiple abductive explanations. Therefore, identifying a subclass of “preferred explanations” is a relevant problem. A typical approach is to “prefer” explanations that are, in some sense, simple. Several concepts of simplicity were considered in the literature, most notably those based on minimality with respect to inclusion and cardinality. We adopt, as a measure of the quality of an explanation, its degree of arbitrariness that can be briefly described as the number of arbitrary assumptions that have been made to derive the explanation. The more arbitrary the explanation, the less appealing it is, with explanations having no arbitrariness, called constrained, being the preferred ones. In this article, we present a technique that, for a special class of theories, computes constrained explanations. It is based on a rewriting of the theory and the observation into a disjunctive logic program with negation so that the constrained explanations correspond to a subset of its stable models. The proposed technique lays the foundation for using ASP solvers to compute constrained explanations.

IJCAI Conference 2021 Conference Paper

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

  • Emilio Gamba
  • Bart Bogaerts
  • Tias Guns

We build on a recently proposed method for explaining solutions of constraint satisfaction problems. An explanation here is a sequence of simple inference steps, where the simplicity of an inference step is measured by the number and types of constraints and facts used, and where the sequence explains all logical consequences of the problem. We build on these formal foundations and tackle two emerging questions, namely how to generate explanations that are provably optimal (with respect to the given cost metric) and how to generate them efficiently. To answer these questions, we develop 1) an implicit hitting set algorithm for finding optimal unsatisfiable subsets; 2) a method to reduce multiple calls for (optimal) unsatisfiable subsets to a single call that takes constraints on the subset into account, and 3) a method for re-using relevant information over multiple calls to these algorithms. The method is also applicable to other problems that require finding cost-optimal unsatiable subsets. We specifically show that this approach can be used to effectively find sequences of optimal explanation steps for constraint satisfaction problems like logic grid puzzles.

IJCAI Conference 2021 Conference Paper

On the Relation Between Approximation Fixpoint Theory and Justification Theory

  • Simon Marynissen
  • Bart Bogaerts
  • Marc Denecker

Approximation Fixpoint Theory (AFT) and Justification Theory (JT) are two frameworks to unify logical formalisms. AFT studies semantics in terms of fixpoints of lattice operators, and JT in terms of so-called justifications, which are explanations of why certain facts do or do not hold in a model. While the approaches differ, the frameworks were designed with similar goals in mind, namely to study the different semantics that arise in (mainly) non-monotonic logics. The First contribution of our current paper is to provide a formal link between the two frameworks. To be precise, we show that every justification frame induces an approximator and that this mapping from JT to AFT preserves all major semantics. The second contribution exploits this correspondence to extend JT with a novel class of semantics, namely ultimate semantics: we formally show that ultimate semantics can be obtained in JT by a syntactic transformation on the justification frame, essentially performing some sort of resolution on the rules.

KR Conference 2020 Conference Paper

Inputs, Outputs, and Composition in the Logic of Information Flows

  • Heba Aamer
  • Bart Bogaerts
  • Dimitri Surinx
  • Eugenia Ternovska
  • Jan Van den Bussche

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this paper is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution of this paper is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs, and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded-variable FO.

AAAI Conference 2019 Conference Paper

Weighted Abstract Dialectical Frameworks through the Lens of Approximation Fixpoint Theory

  • Bart Bogaerts

Weighted abstract dialectical frameworks (wADFs) were recently introduced, extending abstract dialectical frameworks to incorporate degrees of acceptance. In this paper, we propose a different view on wADFs: we develop semantics for wADFs based on approximation fixpoint theory, an abstract algebraic theory designed to capture semantics of various non-monotonic reasoning formalisms. Our formalism deviates from the original definition on some basic assumptions, the most fundamental is that we assume an ordering on acceptance degrees. We discuss the impact of the differences, the relationship between the two versions of the formalism, and the advantages each of the approaches offers. We furthermore study complexity of various semantics.

IJCAI Conference 2018 Conference Paper

Exploiting Justifications for Lazy Grounding of Answer Set Programs

  • Bart Bogaerts
  • Antonius Weinzierl

Answer set programming (ASP) is an established knowledge representation formalism. Lazy grounding avoids the so-called grounding bottleneck of ASP by interleaving grounding and solving; this technique was recently extended to work with conflict-driven clause learning. Unfortunately, it often happens that such a lazy grounding ASP system, at the fixpoint of the evaluation, arrives at an assignment that contains literals that are true but unjustified. The system then is unable to determine the actual causes of the situation and falls back to chronological backtracking, potentially wasting an exponential amount of time. In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree. Contributions include a rephrasing of lazy grounding in terms of justifications and algorithms to construct relevant justifications without grounding. Initial experiments indicate that the newly developed techniques indeed allow for an exponential speed-up.

IJCAI Conference 2017 Conference Paper

Safe Inductions: An Algebraic Study

  • Bart Bogaerts
  • Joost Vennekens
  • Marc Denecker

In many knowledge representation formalisms, a constructive semantics is defined based on sequential applications of rules or of a semantic operator. These constructions often share the property that rule applications must be delayed until it is safe to do so: until it is known that the condition that triggers the rule will remain to hold. This intuition occurs for instance in the well-founded semantics of logic programs and in autoepistemic logic. In this paper, we formally define the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. For the latter, we show that safe inductions manage to capture the intended meaning of a class of theories on which all classical constructive semantics fail.

IJCAI Conference 2017 Conference Paper

Semantics for Active Integrity Constraints Using Approximation Fixpoint Theory

  • Bart Bogaerts
  • Luís Cruz-Filipe

Active integrity constraints (AICs) constitute a formalism to associate with a database not just the constraints it should adhere to, but also how to fix the database in case one or more of these constraints are violated. The intuitions regarding which repairs are “good” given such a description are closely related to intuitions that live in various areas of non-monotonic reasoning. In this paper, we apply approximation fixpoint theory, an algebraic framework that unifies semantics of non-monotonic logics, to the field of AICs. This results in a new family of semantics for AICs, of which we study semantics and relationships to existing semantics. We argue that the AFT-well-founded semantics has some desirable properties.

KR Conference 2016 Conference Paper

Declarative solver development: Case studies

  • Bart Bogaerts
  • Tomi Janhunen
  • Shahab Tasharrofi

The formalisms for knowledge representation and reasoning (KR&R) typically have a variety of semantics, each one having its particular application scenarios. However, the KR&R community cannot readily benefit from such a variety due to a lack of efficient solver technology. This is partly caused by the fact that solver development is laborious and even accomplishing a working prototype can form a major effort. In this paper, we introduce a new framework that enables us to declaratively specify a given semantics in second-order logic and to automatically generate a solver from that specification. Hence, KR&R researchers can rapidly develop a solver prototype for their new/existing semantics with a minimal effort. Technically, our framework builds on a recent approach for nesting SAT solvers based on lazy clause generation. We evaluate our framework in the context of Dung’s argumentation frameworks, logic programming, and propositional logic subject to standard and non-standard semantics. We show for each of those formalisms that one can easily specify its semantics using a few second-order sentences and that one can effectively obtain a solver for that semantics using our automated solver generation procedure. For instance, in the case of argumentation frameworks, we obtain 16 different solvers, each solving one of four inference tasks for one of four major argumentation semantics and show that our solvers (slightly) outperform the best solver from the last system competition despite not being tuned for argumentation instances.

IJCAI Conference 2016 Conference Paper

Distributed Autoepistemic Logic and its Application to Access Control

  • Pieter Van Hertum
  • Marcos Cramer
  • Bart Bogaerts
  • Marc Denecker

In this paper we define and study an extension of autoepistemic logic (AEL) called distributed autoepistemic logic (dAEL) with multiple agents that have full introspection in their own knowledge as well as in that of others. This mutual full introspection between agents is motivated by an application of dAEL in access control. We define 2- and 3-valued semantic operators for dAEL. Using these operators, approximation fixpoint theory, an abstract algebraic framework that unifies different knowledge representation formalisms, immediately yields us a family of semantics for dAEL, each based on different intuitions that are well-studied in the context of AEL. The application in access control also motivates an extension of dAEL with inductive definitions (dAEL(ID)). We explain a use-case from access control to demonstrate how dAEL(ID) can be fruitfully applied to this domain and discuss how well-suited the different semantics are for the application in access control.

IJCAI Conference 2016 Conference Paper

Relevance for SAT(ID)

  • Joachim Jansen
  • Bart Bogaerts
  • Jo Devriendt
  • Gerda Janssens
  • Marc Denecker

Inductive definitions and justifications are well-studied concepts. Solvers that support inductive definitions have been developed, but several of their computationally nice properties have never been exploited to improve these solvers. In this paper, we present a new notion called relevance. We determine a class of literals that are relevant for a given definition and partial interpretation, and show that choices on irrelevant atoms can never benefit the search for a model. We propose an early stopping criterion and a modification of existing heuristics that exploit relevance. We present a first implementation in MinisatID and experimentally evaluate our approach, and study how often existing solvers make choices on irrelevant atoms.

AAAI Conference 2015 Conference Paper

Grounded Fixpoints

  • Bart Bogaerts
  • Joost Vennekens
  • Marc Denecker

Algebraical fixpoint theory is an invaluable instrument for studying semantics of logics. For example, all major semantics of logic programming, autoepistemic logic, default logic and more recently, abstract argumentation have been shown to be induced by the different types of fixpoints defined in approximation fixpoint theory (AFT). In this paper, we add a new type of fixpoint to AFT: a grounded fixpoint of lattice operator O: L → L is defined as a lattice element x ∈ L such that O(x) = x and for all v ∈ L such that O(v ∧ x) ≤ v, it holds that x ≤ v. On the algebraical level, we show that all grounded fixpoints are minimal fixpoints approximated by the well-founded fixpoint and that all stable fixpoints are grounded. On the logical level, grounded fixpoints provide a new mathematically simple and compact type of semantics for any logic with a (possibly non-monotone) semantic operator. We explain the intuition underlying this semantics in the context of logic programming by pointing out that grounded fixpoints of the immediate consequence operator are interpretations that have no non-trivial unfounded sets. We also analyse the complexity of the induced semantics. Summarised, grounded fixpoint semantics is a new, probably the simplest and most compact, element in the family of semantics that capture basic intuitions and principles of various non-monotonic logics.

IJCAI Conference 2015 Conference Paper

Partial Grounded Fixpoints

  • Bart Bogaerts
  • Joost Vennekens
  • Marc Denecker

Approximation fixpoint theory (AFT) is an algebraical study of fixpoints of lattice operators. Recently, AFT was extended with the notion of a grounded fixpoint. This type of fixpoint formalises common intuitions from various application domains of AFT, including logic programming, default logic, autoepistemic logic and abstract argumentation frameworks. The study of groundedness was limited to exact lattice points; in this paper, we extend it to the bilattice: for an approximator A of O, we define A-groundedness. We show that all partial Astable fixpoints are A-grounded and that the Awell-founded fixpoint is uniquely characterised as the least precise A-grounded fixpoint. We apply our theory to logic programming and study complexity.