ABSTRACT. In the extended abstract we report about a many-valued and multi-preferential conditional logic with typicality developed in https://academic.oup.com/logcom/article/36/2/exaf063/8442302, which is based on a multi-preferential semantics and is proven to be a generalization of KLM preferential semantics to the many-valued case. The multi-preferential semantics provides a preferential interpretation to gradual argumentation, considering both weighted and non-weighted argumentation graphs. The approach allows for conditional reasoning over arguments and boolean combination of arguments, with respect to some gradual semantics, through the verification of graded (strict or defeasible) implications over argumentation graphs.

ABSTRACT. We study realizability in terms of stable labellings for classical Dung frameworks. Although there is a close relationship between extension-based and labelling-based semantics, realizability with respect to labellings is significantly more challenging. This is due to the explicit treatment of non-accepted arguments (i.e., undecided and rejected ones), which fixes the argument set of any witnessing framework and rules out auxiliary arguments commonly used in constructions for extension-based semantics. Consequently, existing characterizations for extension-based semantics can only be reused to a limited extent. Towards a characterization we introduce so-called candidate sets. This notion captures maximal conflict-free sets that have the potential to become stable but should not. These sets constitute a central obstacle in our approach. In the absence of such sets, we provide a characterization theorem. For the general case, we identify a canonical over-approximating framework such that every stably realizable labelling set admits a witnessing argumentation framework obtainable as a subgraph of it. However, we observed an inherent non-determinism for our proposed method. Finally, we identified a connection between labelling-based realizability and the long-standing open problem of compact stable extension realizability.

ABSTRACT. We introduce tenability, a dialogue-based semantics for Dung-style Argumentation Frameworks that formalizes when a designated argument (or a set of arguments) can be maintained in debate by a proponent against any legitimate (conflict-free) attack which the opponent may present. The approach naturally corresponds to one-shot debate scenarios in which a uniform defense against all possible counterarguments is not required, and is robust with respect to further constraints placed on the rules of the dialogue game.

ABSTRACT. Partial expansion operators were presented in Zhang (2018) as intermediate operators that, when combined with choice contraction, lead to choice revision operators. We propose analyzing partial expansion operators in their own right, generalizing the original proposal, and studying different alternatives for this family. In doing so, we also point out a flaw in the original definition where some postulates are incompatible, and propose how to amend it by considering new postulates that maintain the original purpose of these operators.

ABSTRACT. We study the computational aspects of \emph{epistemic preference relations} in non-classical logics, particularly \emph{linear temporal logic} (LTL). Epistemic preferences form the backbone of \emph{belief contraction operators}, which describe how to rationally relinquish obsolete beliefs. These preference relations have to satisfy certain innocuous conditions; and constructing such relations is usually assumed to be a trivial process. However, in the case of LTL, where relations are represented with Büchi automata, we show that this is a challenging task: the core condition, which guarantees the success of contraction, is in fact \emph{undecidable}. Towards achieving effective LTL belief contraction, we then propose several concrete constructions of novel preference relations that satisfy the required conditions \emph{by design}. These constructions include, among others, (1) generalisations of distance measures (e.g.\ Dalal) beyond the classical setting, as well as (2) the ability to hierarchically compose different preference relations. Our results not only provide rich families of preference relations for LTL, but also generalise the limited pool of concrete preference relations for the classical cases, allowing us to go beyond Dalal to achieve full rationality.

ABSTRACT. In this paper we investigate truth-tracking properties of iterated belief revision methods. We focus on the multi-agent context when several agents revise their plausibility spaces simultaneously and arrive at joint beliefs through merge. We investigate the convergence properties of such a process: given a distributed sequence of information that truthfully and completely describes the actual world, will the process converge to true beliefs about the actual world? We focus on two methods that were previously proven to work for single-agents truth-tracking based on belief revision: conditioning and lexicographic revision. We show that some of the positive results carry on to our multi-agent setting. We introduce a novel kind of belief merge operation that is memory-based. We also study how to deal with occasional erroneous information by applying priority merge techniques.

ABSTRACT. Machine learning classifiers work by being given a dataset of instances described by observed properties and labels, and then learning how to predict the label of a new instance. Some methods do this in an interpretable way, producing simple if-then rules that a person can read directly. A learned rule set may say that one feature pattern usually supports label~1, except when a more specific pattern supports label~0. People naturally read such outputs as defaults and exceptions, but a readable rule set is not automatically a defeasible theory: it may look defeasible without any formal guarantee that its predictions correspond to principled nonmonotonic reasoning. This paper provides such a guarantee. We identify a single structural condition on learned conjunctive rule sets for binary classification, called strict global exception closure, under which every prediction the classifier makes is provably a defeasible entailment. To make this precise, we translate the rule set into a defeasible knowledge base in the framework of Kraus, Lehmann, and Magidor (KLM), a standard approach to reasoning with rules that can have exceptions. We then prove that, for every fully observed instance, the classifier's Most Specific Wins prediction agrees exactly with the entailment of the translated knowledge base under three well-known KLM-style formalisms: Rational Closure, Lexicographic Closure, and System~W. The classifier is therefore not merely interpretable; it is a formally grounded defeasible theory. We further show that strict global exception closure is the only condition that needs to be checked directly: one additional property follows from it logically, and another is restored by a prediction-preserving pruning step. We analyse what breaks when the condition fails, revealing insights for both machine learning and nonmonotonic reasoning. Experiments on several benchmark datasets confirm that the condition arises naturally from decision tree extraction, producing rule sets with nontrivial exception hierarchies at competitive accuracy.

ABSTRACT. Structural causal models (SCMs) are the default substrate for counterfactual explanation, responsibility, and causal recourse in the Halpern-Pearl account of actual causation. In SCMs, interventions quantify over arbitrary assignments to endogenous variables. However, in socio-technical systems, agent actions (and hence responsibility) are constrained by procedures, authority, permissions, and coordination requirements. Thus counterfactual `alternatives' may include states that the relevant agent could not have feasibly executed. We characterize this intervention--feasibility gap and show how it can yield misplaced responsibility attributions or infeasible recourse recommendations even when the underlying SCM is descriptively adequate. We propose a principled restriction of intervention admissibility via \emph{Resource-aware SCMs} (R-SCMs). Without abandoning SCM semantics, we equip models with agent-controlled endogenous variables, enablement predicates evaluated against the acting agent's observation, and consumable resources modelled by a partial commutative monoid. We interpret agent-relative counterfactual feasibility via executable, resource-consuming intervention traces (a sequence of actions) and prove basic properties of R-SCMs (well-definedness, monotonicity, conservativity under trivial feasibility). Finally, we prove \textsf{NP-completeness} of the bounded-horizon feasibility decision problem for R-SCMs.

ABSTRACT. Reinforcement learning (RL) is notoriously sample-inefficient and opaque, a problem that error-prone techniques like reward shaping or feature engineering often fail to resolve. In this work, we introduce a method to induce temporal advice from expert demonstrations via Inductive Logic Programming. We then show how this advice, transformed to answer set automata, enables the transparent guidance of RL agents. The advice is symbolic and interpretable, being expressed in linear temporal logic over finite traces with answer set programming queries. Formula-learning experiments show that our encoding successfully induces generalizable temporal advice from executions of a partially observable environment. We apply the learned advice to guide an RL agent, benchmarking its performance against three baselines: standard unguided Q-learning, guidance via time-independent rules, and guidance using persistent macro-action rules. Our findings show that temporal advice learned on small maps transfers successfully to larger instances, outperforming standard Q-learning and both baseline guidance approaches in convergence rate.