Online fraud detection poses increasing challenges as malicious actors continuously adapt their tactics to evade recognition. Conventional detection systems, whether rule-based or machine learning–driven, process transactions in isolation and fail to capture the interdependent, dynamic, and adversarial nature of fraud. Graph Neural Networks (GNNs) have shown promise in modeling such relational data, yet most existing models assume homophily, neglect temporal evolution, and rely heavily on supervision or graph restructuring, therefore limiting their robustness in real-world fraud scenarios characterized by heterophily, class imbalance, and camouflage. To address these issues, we propose HOT-GNN, a Heterophily Outlier Temporal-aware Graph Neural Network designed to detect complex and camouflaged fraud patterns in heterogeneous, evolving graphs. HOT-GNN introduces a Hybrid Outlier-aware Similarity (HOS) mechanism that quantifies structural, semantic, and anomaly alignment to distinguish genuine from deceptive relations. It further employs a decoupled multi-view message-passing framework to separately aggregate information from homophilic and heterophilic neighbors, preventing noise propagation and preserving discriminative heterophilic signals. Temporal positional encoding and relation-specific modules enable HOT-GNN to capture behavioral dynamics without requiring explicit dynamic graph snapshots. Extensive experiments on four benchmark datasets demonstrate that HOT-GNN achieves consistent improvements over state-of-the-art GNN-based detectors.

We define a new formalism, called$\beth$CHR, which generalises the CHR formalism by introducing some meta-constraints, called behaviors, allowing us to easily specify problems modelling an adversary. We define a new language with a syntax and a proof-theoretical semantics as an extension of the syntax and semantics of the CHR language.

E-health systems have revolutionized healthcare by enabling efficient data sharing and management. However, they face significant security and privacy challenges, including unauthorized access, data breaches, identity fraud, and insurance fraud. Existing solutions attempt to address these issues but suffer from single points of failure, lack of patient-defined access control, and inadequate privacy-preserving mechanisms. This paper proposes a dual-blockchain architecture integrated with Self-Sovereign Identity and Zero-Knowledge Proofs to enhance security, privacy, and fraud resilience. The framework employs Decentralized Identifiers and Verifiable Credentials for secure authentication while leveraging the InterPlanetary File System for decentralized Electronic Health Records storage. By addressing the limitations of current systems, the proposed solution ensures a more secure, scalable, and privacy-preserving e-health environment.

In this article, the QCSP (Quantified Constraint Satisfaction Problems) framework is shifted to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic generation of quantifiers and access to user-defined constraints. QCSP offers a natural framework to express PSPACE problems as finite two-players games. But to define a QCSP model, the alternation of quantifiers must be formerly known and cannot be built dynamically even if the worst case will not occur. To overcome this issue, the new QCHR formalism, that allows to generate quantifiers dynamically during the solving, is defined. QCHR models exhibit state-of-the-art performances on a priori fixed alternation of quantifiers and outperforms previous QCSP approaches when the generation of quantifiers is dynamic.

Abstract Reasoning about dynamic systems with a fine-grained temporal and numeric resolution presents significant challenges for logic-based approaches like Answer Set Programming (ASP). To address this, we introduce and elaborate upon a novel temporal and constraint-based extension of the logic of Here-and-There and its nonmonotonic equilibrium extension, representing, to the best of our knowledge, the first approach to nonmonotonic temporal reasoning with constraints specifically tailored for ASP. This expressive system is achieved by a synergistic combination of two foundational ASP extensions: the linear-time logic of Here-and-There, providing robust nonmonotonic temporal reasoning capabilities, and the logic of Here-and-There with constraints, enabling the direct integration and manipulation of numeric constraints, among others. This work establishes the foundational logical framework for tackling complex dynamic systems with high resolution within the ASP paradigm.

Constraint Answer Set Programming (CASP) combines Answer Set Programming (ASP) and Constraint Programming (CP) to offer a powerful framework for solving complex problems. While there exists various ways to represent CASP problems, fusing both in a way that benefits both communities remains difficult to this day. To address this, we propose to extend the Well Founded Semantics (WFS) to propositional constraint logic programs. To this end, we present an iterative process involving elementary rewriting transformations to simplify the program iteratively. Our method aims to give a better description of the interactions between ASP and CP inside of a constraint logic program.

The paper deals with base revision for Answer Set Programming (ASP). Base revision in classical logic is done by the removal of formulas. Exploiting the non-monotonicity of ASP allows one to propose other revision strategies, namely addition strategy or removal and/or addition strategy. These strategies allow one to define families of rule-based revision operators. The paper presents a semantic characterization of these families of revision operators in terms of answer sets. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results.

Belief base revision has been studied within the answer set programming framework. We go a step further by introducing uncertainty and studying belief base revision when beliefs are represented by possibilistic logic programs under possibilistic answer set semantics and revised by certain input. The paper proposes two approaches of rule-based revision operators and presents their semantic characterization in terms of possibilistic distribution. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results.

Abstract The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first-order logic program with default negation. In a preliminary step, this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this article, the projectASPeRiX. is presented as a first-order forward chaining approach for Answer Set Computing. This project was among the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first-order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solverASPeRiX. are detailed and some experiments and comparisons with existing systems are provided.

We propose in this article a functional semantics for non-prenex Quantified Boolean Formulae. The proposed formalism is symmetrical for validity and non-validity and allows to give different interpretations to the quantifiers. This formalism is also sufficiently flexible to allow the definition of a certificate for the search-based algorithms for non-prenex QBF.

Quantified Constraint Satisfaction Problems (QCSP) are a generalization of Constraint Satisfaction Problems (CSP) in which variables may be quantified existentially and universally. QCSP offers a natural framework to express PSPACE problems as finite two-player games or planning under uncertainty. State-of-the-art QCSP solvers have an important drawback: they explore much larger combinatorial spaces than the natural search space of the original problem since they are unable to recognize that some sub-problems are necessarily true. We introduce a new tool, inspired by the cut rule of Prolog as a tool under responsibility of the designer of the QCSP, to prune those parts of the search space which are by construction known to be useless. We use this new tool to restore on one hand the annihilator property of true for disjunction in QCSP solver and, on the other hand, to prune the search space in two-player games. It is a simple solution to use efficiently QCSP to design finite two-player games without restricting the QCSP language. This tool does not need to modify the QCSP solver but has only one requirement: be able to tell the QCSP solver that the current QCSP is solved. Our QCSP solver built over Ge Code, a CSP library, obtained very good results compared to state-of-the-art QCSP solvers.

We propose in this article a translation from quantified Boolean formulae to answer set programming. The computation of a solution of a quantified Boolean formula is then equivalent to the computation of a stable model for a normal logic program. The case of unquantified Boolean formulae is also considered since it is equivalent to the case of quantified Boolean formulae with only existential quantifiers.

In Artificial Intelligence, Default Logic is recognized as a powerful framework for knowledge representation when one has to deal with incomplete information. Its expressive power is suitable for nonmonotonic reasoning, but the counterpart is its very high level of theoretical complexity. Today, some operational systems are able to deal with real world applications. However finding a default logic extension in a practical way is not yet possible in whole generality. This paper shows how modern heuristics such as genetic algorithms and local search techniques can be used and combined to build an automated default reasoning system. We give a general description of the required basic components and we exhibit experimental results.