Author name cluster

Emmanuel Lonca

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Targeting in Multi-Criteria Decision Making

Nicolas Schwind
Patricia Everaere
Sébastien Konieczny
Emmanuel Lonca

In this work, we introduce the notion of targeting for multi-criteria decision making. The problem involves selecting the best alternatives related to one particular alternative, called the target. We use an axiomatic approach to this problem by establishing properties that any targeting method should satisfy. We present a representation theorem and show that satisfying the main properties of targeting requires aggregating the evaluations of the alternatives related to the target. We propose various candidate targeting methods and examine the properties satisfied by each method.

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KR Conference 2025 Conference Paper

Counterexample-Guided Abstraction Refinement for Assumption-based Argumentation

Jean Marie Lagniez
Emmanuel Lonca
Jean-Guy Mailly

Assumption-Based Argumentation (ABA) is a prominent formalism for structured argumentation, widely applied in domains such as healthcare, law, and robotics. Despite its inherent computational complexity, ABA has seen the development of effective techniques that successfully address key tasks, including evaluating the acceptability of literals and computing framework extensions. These approaches typically involve translating the initial ABA framework into an intermediate formalism, such as an Answer Set Program or an Abstract Argumentation Framework, which is then encoded into a Boolean satisfiability (SAT) problem. However, this translation can lead to large and complex intermediate representations, posing challenges for state-of-the-art SAT solvers. In this work, we propose a Counterexample-Guided Abstraction Refinement (CEGAR) approach that bypasses the initial translation step, at the cost of incrementally discovering certain ABA constraints that are not explicitly captured in the initial SAT encoding. We analyze the performance of our method and demonstrate that it outperforms state-of-the-art approaches on specific problem classes, while remaining competitive with the best existing solvers more broadly.

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AAMAS Conference 2024 Conference Paper

A SAT-based Approach for Argumentation Dynamics

Jean-Marie Lagniez
Emmanuel Lonca
Jean-Guy Mailly

In the realm of multi-agent systems, argumentative dialogues for persuasion and negotiation involve autonomous agents exchanging arguments, necessitating continual re-evaluation of argument acceptability. This study introduces a novel approach using modern SAT solving techniques to dynamically reassess the acceptability status of arguments, aligning with various classical semantics. Our method uses the assumption mechanism in SAT solvers, distinguished by minimal assumptions, ensuring practicality.

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KR Conference 2024 Conference Paper

Leveraging Decision-DNNF Compilation for Enumerating Disjoint Partial Models

Jean-Marie Lagniez
Emmanuel Lonca

The All-Solution Satisfiability Problem (AllSAT) extends SAT by requiring the identification of all possible solutions for a propositional formula. In practice, enumerating all complete models is often infeasible, making the identification of partial models essential for generating a concise representation of the solution set. Deterministic Decomposable Negation Normal Form (d-DNNF) serves as a language for representation known to offer polynomial-time algorithms for model enumeration. Specifically, when a propositional formula is encoded in d-DNNF, it enables iterative model enumeration with polynomial delay between models. However, despite the existence of theoretical algorithms for this purpose, no available implementations are currently accessible. Furthermore, these theoretical approaches are nearly impractical as they solely yield complete models. We introduce a novel algorithm that maintains a polynomial delay between partial models while significantly enhancing efficiency compared to baseline approaches. Furthermore, through experimental validation, we demonstrate the superiority of compiling a CNF formula Σ into a d-DNNF formula Σ′ and subsequently enumerating models of Σ′ over existing state-of-the-art methodologies for CNF partial model enumeration.

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AIJ Journal 2020 Journal Article

Definability for model counting

Jean-Marie Lagniez
Emmanuel Lonca
Pierre Marquis

We define and evaluate a new preprocessing technique for propositional model counting. This technique leverages definability, i. e. , the ability to determine that some gates are implied by the input formula Σ. Such gates can be exploited to simplify Σ without modifying its number of models. Unlike previous techniques based on gate detection and replacement, gates do not need to be made explicit in our approach. Our preprocessing technique thus consists of two phases: computing a bipartition 〈 I, O 〉 of the variables of Σ where the variables from O are defined in Σ in terms of I, then eliminating some variables of O in Σ. Our experiments show the computational benefits which can be achieved by taking advantage of our preprocessing technique for model counting.

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IJCAI Conference 2018 Conference Paper

Artificial Intelligence Conferences Closeness

Sébastien Konieczny
Emmanuel Lonca

We study the evolution of Artificial Intelligence conference closeness, using the coscinus tool. Coscinus computes the closeness between publication supports using the co-publication habits of authors: the more authors publish in two conferences, the closer these two conferences. In this paper we perform an analysis of the main Artificial Intelligence conferences based on principal components analysis and clustering performed on this closeness relation.

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ECAI Conference 2016 Conference Paper

Fixed-Parameter Tractable Optimization Under DNNF Constraints

Frédéric Koriche
Daniel Le Berre
Emmanuel Lonca
Pierre Marquis

Minimizing a cost function under a set of combinatorial constraints is a fundamental, yet challenging problem in AI. Fortunately, in various real-world applications, the set of constraints describing the problem structure is much less susceptible to change over time than the cost function capturing user's preferences. In such situations, compiling the set of feasible solutions during an offline step can make sense, especially when the target compilation language renders computationally easier the generation of optimal solutions for cost functions supplied "on the fly", during the online step. In this paper, the focus is laid on Boolean constraints compiled into DNNF representations. We study the complexity of the minimization problem for several families of cost functions subject to DNNF constraints. Beyond linear minimization which is already known to be tractable in the DNNF language, we show that both quadratic minimization and submodular minization are fixed-parameter tractable for various subsets of DNNF. In particular, the fixed-parameter tractability of constrained submodular minimization is established using a natural parameter capturing the structural dissimilarity between the submodular cost function and the DNNF representation.

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IJCAI Conference 2016 Conference Paper

Improving Model Counting by Leveraging Definability

Jean-Marie Lagniez
Emmanuel Lonca
Pierre Marquis

We present a new preprocessing technique for propositional model counting. This technique leverages definability, i. e. , the ability to determine that some gates are implied by the input formula Σ . Such gates can be exploited to simplify Σ without modifying its number of models. Unlike previous techniques based on gate detection and replacement, gates do not need to be made explicit in our approach. Our preprocessing technique thus consists of two phases: computing a bipartition I, O of the variables of Σ where the variables from O are defined in Σ in terms of I, then eliminating some variables of O in Σ . Our experiments show the computational benefits which can be achieved by taking advantage of our preprocessing technique for model counting.

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SAT Conference 2014 Conference Paper

Detecting Cardinality Constraints in CNF

Armin Biere
Daniel Le Berre
Emmanuel Lonca
Norbert Manthey

Abstract We present novel approaches to detect cardinality constraints expressed in CNF. The first approach is based on a syntactic analysis of specific data structures used in SAT solvers to represent binary and ternary clauses, whereas the second approach is based on a semantic analysis by unit propagation. The syntactic approach computes an approximation of the cardinality constraints AtMost-1 and AtMost-2 constraints very fast, whereas the semantic approach has the property to be generic, i. e. it can detect cardinality constraints AtMost- k for any k, at a higher computation cost. Our experimental results suggest that both approaches are efficient at recovering AtMost-1 and AtMost-2 cardinality constraints.

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