Journal of Artificial Intelligence Research

We consider a class of jump games in which agents of different types occupy the nodes of a graph aiming to maximize the variety of types in their neighborhood. In particular, each agent derives a utility equal to the number of types different from its own in its neighborhood. We show that the jump game induced by the strategic behavior of the agents (who aim to maximize their utility) may in gene…

Text generation has become more accessible than ever, and the growing interest in these systems, especially those using large language models, has spurred a surge in related publications. We provide a systematic literature review comprising 257 papers, covering the period from January 2017 to December 2025. This review categorizes text generation contributions into five main tasks: open-ended tex…

Background : Hierarchical Classification (HC) has long been recognized for improving predictive performance by exploiting relationships between classes. However, most tabular multi-class datasets lack predefined class hierarchies, limiting the broader applicability of hierarchy-aware learning methods. Objectives : This study introduces HiGEC (Hierarchy Generation and Exploitation for Classificati…

The goal of learning to hash (L2H) is to derive data-dependent hash functions from a given data distribution to map data from the input space to a binary coding space. Despite the success of L2H, two observations have cast doubt on the source of its power, i.e., learning. First, a recent study shows that a version of locality-sensitive hashing without learning can achieve comparable accuracy to L…

This work introduces a new dimension of argumentative quality, termed Minimal Dialectical Quality (MDQ), which requires an argumentation to coherently support its main claims through adequate justifications and, when necessary, explicit rebuttals. MDQ provides a less subjective notion of argumentation quality than many existing approaches, as it relies exclusively on information contained in the …

Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g., safety or structural constraints. As such, it represents one of the most promising avenues for reliable and trustworthy AI. The core idea behind NeSy AI is to combine neural and symbolic steps: neural networks are typically responsible for mapping low-level inputs into high-…

Background : Deep neural networks increasingly power language, vision, and decision systems, yet many deployments require explanations that are faithful, compositional, and governance-ready. Symbolic techniques promise these properties, but the literature mixes post-hoc extraction, knowledge injection, and intrinsically hybrid designs without a unifying view. Objectives : We provide a systematic …

This paper investigates complex argumentation settings, called weighted higher-order argumentation frameworks (wHO-AFs), where both arguments and attacks carry initial weights and may be subject to attacks from arguments. It focuses on developing gradual semantics capable of rationally evaluating these elements by assigning each argument and attack a numerical value, representing their respective…

Given the ubiquity of negative campaigning in recent political elections, we find it important to study its properties from a theoretical computational perspective. To this end, we present a model where elections can be manipulated by convincing voters to demote specific non-favored candidates, and study its properties in the classic setting of scoring rules. When the goal is constructive (making…

Federated Learning (FL) enables privacy-preserving model training across heterogeneous distributed systems, such as smartgrid forecasting or traffic-flow prediction from geographically dispersed sensors and devices. A key challenge in such settings is capturing client-specific patterns while addressing data heterogeneity and uncertainty at scale. Existing approaches, including Bayesian Neural Net…

Revising what an agent knows in response to new information is a central problem in formal epistemology. In doxastic logics such as KD45, belief revision proceeds by reordering plausibility: the agent simply re-ranks which worlds it considers most credible. This strategy fails for S5 knowledge. Because knowledge is factive ( K φ → φ), an agent cannot come to know phi merely by finding φ-worlds mo…

We study multivariate decision trees (MDTs), in particular, classes of MDTs determined by the language of relations that can be used to split feature space. An abductive explanation (AXp) of the classification of a particular instance, viewed as a set of feature-value assignments, is a minimal subset of the instance which is sufficient to lead to the same decision. We investigate when finding a s…

Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background knowledge. We introduce an approach that shrinks the hypothesis space before an ILP system searches it. Our approach uses background knowledge to find rules that cannot be in an optimal hypothesis regardless of the tra…

To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning…