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.

Industrial automated reasoning demands the rapid, repeated extraction of insights from complex formulas. Knowledge compilation into the Deterministic Decomposable Negation Normal Form (d-DNNF) addresses this by reducing natively intractable tasks to polynomial-time operations. We present decdnnf_rs, a performant framework for executing advanced reasoning queries directly on d-DNNF circuits. The library provides unified support for Satisfiability, Model Counting, Disjoint Model Enumeration, Direct Access, and Uniform Sampling. Crucially, decdnnf_rs handles dynamic contexts through implicit conditioning via weight propagation, avoiding the computational overhead of explicit graph modification. It also incorporates dynamic smoothness tracking to maintain a compact memory footprint. Bridging theoretical advancements with robust software engineering, decdnnf_rs offers an optimized toolset for exact and stochastic reasoning.

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.

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.

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.

Nowadays, argumentation is a salient keyword in artificial intelligence. The use of argumentation techniques is particularly convenient for thematics such that multiagent systems, where it allows to describe dialog protocols (using persuasion, negotiation, ...) or on-line discussion analysis, it also allows to handle queries where a single agent has to reason with conflicting information (inference in the presence of inconsistency, inconsistency measure). This very rich framework gives numerous reasoning tools, thanks to several acceptability semantics and inference policies. On the other hand, the progress of SAT solvers in the recent years, and more generally the progress on Constraint Programming paradigms, lead to some powerful approaches that permit to tackle theoretically hard problems. The needs of efficient applications to solve the usual reasoning tasks in argumentation, together with the capabilities of modern Constraint Programming solvers, lead us to study the encoding of usual acceptability semantics into logical settings. We propose diverse use of Constraint Programming techniques to develop a software library dedicated to argumentative reasoning. We present a library which offers the advantages to be generic and easily adaptable. We finally describe an experimental study of our approach for a set of semantics and inference tasks, and we describe the behaviour of our solver during the First International Competition on Computational Models of Argumentation.

Summary Variability determines the capability of software applications to be configured and customized. A common need during the development of variability‐intensive systems is the automated analysis of their underlying variability models, for example, detecting contradictory configuration options. The analysis operations that are performed on variability models are often very complex, which hinders the testing of the corresponding analysis tools and makes difficult, often infeasible, to determine the correctness of their outputs, that is, the well‐knownoracle problemin software testing. In this article, we present a generic approach for the automated detection of faults in variability analysis tools overcoming the oracle problem. Our work enables the generation of random variability models together with the exact set of valid configurations represented by these models. These test data are generated from scratch using stepwise transformations and assuring that certain constraints (a.k.a.metamorphic relations) hold at each step. To show the feasibility and generalizability of our approach, it has been used to automatically test several analysis tools in three variability domains: feature models, common upgradeability description format documents and Boolean formulas. Among other results, we detected 19 real bugs in 7 out of the 15 tools under test. Copyright © 2015 John Wiley & Sons, Ltd.