ABSTRACT. The Relativity of Causal Knowledge (RCK) explains how agents with different structural causal models can exchange causal knowledge through a shared interventionally consistent abstraction, or backbone. We ask the prior identification question that this transport mechanism presupposes: when is that backbone determined by the agents' private causal knowledge? In the basic two-agent common-effect, case, two private causes influence one shared outcome and each agent identifies only the single-cause causal marginal relevant to its own perspective. We show that, under standard compatibility, non-degeneracy, and local overlap assumptions, those local causal marginals do not identify a unique edge-level backbone. Infinitely many joint intervention kernels can induce exactly the same private reports while disagreeing on joint interventions. The obstruction is therefore not in defining RCK restriction or extension maps once an edge value is fixed; it is in identifying the common edge value to which those maps should apply. We then give a conditional recovery result. Additive separability removes the hidden interaction degree of freedom, but observational residual summaries remain insufficient. Identification becomes possible when agents communicate causally identified response functions. An education value-added example illustrates why this is first a communication problem, and only then a policy-composition problem.

ABSTRACT. A core challenge for real-world agents is determining which aspects of a decision problem are most relevant – that is, deciding what to pay attention to. Salience profoundly influences human decision-making, often leading to choices that diverge from normative rationality. In this extended abstract, we sketch a formal model that captures how salience modulates preferences and drives changes in preference. We provide a representation theorem characterising preference relations shaped by salience-weighted features. This framework provides a mathematical starting point for studying preference change under attentional modulation and for designing systems that can effectively guide human decision-making in complex, multi-faceted environments. At the same time, we highlight important considerations about the responsible design of influence strategies, particularly in contexts where subtle preference shaping may have unintended or harmful consequences.

ABSTRACT. Robots are increasingly being used in different application domains, aided in large part by the deep networks and foundation models that are now considered to be state of the art for many problems in AI and robotics. However, these methods and models are resource-hungry and opaque, and they provide arbitrary decisions in previously unknown situations, whereas practical applications often require transparent, multi-step, multi-level decision-making and collaboration under resource constraints and open world uncertainty. I argue that to leverage the full potential of AI in robotics, we need to revisit fundamental principles such as abstraction, ecological rationality, interactive learning and explainable agency, which can be traced back to the work of the early pioneers of AI. I would like to describe how we can combine the complementary strengths of knowledge-based and data-driven methods for reasoning and learning by embedding these principles in architectures developed for robots.

ABSTRACT. In this work we introduce proof-theoretic tools to the investigation of logics of qualitative probability which, to the best of our knowledge are missing in the literature. We report on ongoing work, introducing weaker systems for qualitative probability based on the model of the polynomial approximation of classical logic that have polynomial computational complexity and approximate full qualitative reasoning.

ABSTRACT. This paper studies strategic communication under ambiguity within a cheap talk framework, extending standard models beyond probabilistic beliefs. We represent uncertainty using Dempster–Shafer mass functions, allowing for non-additive and ambiguous beliefs, and analyze how a sender optimally communicates when unsure how a receiver resolves such ambiguity. Considering neutral, robust, and optimistic attitudes, we characterize equilibrium behavior and show how ambiguity shapes persuasion strategies and outcomes.

ABSTRACT. Wepropose an ASP approach to reasoning about Halpern-Pearl actual causes and argue that our encoding is simpler, conceptually more appropriate, and more general than the existing approach in literature.

ABSTRACT. Joe Halpern and I started about causality and harm in voting contexts first in July 2025 in Dusseldorf, and longer in December 2025 in Paris, where Joe had been invited to give a seminar, which was excellent. We talked about voting and causality for several hours and he promised to send me his latest draft, which he did on December 28.

ABSTRACT. We study the problem of computing actual causes for neural network (NN) predictions under structured input dependencies. Existing explanation methods typically assume feature independence, which can produce misleading explanations when inputs are causally related. To address this limitation, we formalize explanations using Halpern and Pearl actual causality within Structural Causal Models (SCMs). We reduce the computation of actual causes to a NN verification problem by combining differentiable relaxations with branch-and-bound verification techniques. Our preliminary experiments indicate that the proposed method is effective and scalable for computing causal explanations of neural network predictions.