An Argumentative Explanation Framework for Generalized Reason Model with Inconsistent Precedents

Precedential constraint is one foundation of case-based reasoning in AI and Law. It generally assumes that the underlying set of precedents must be consistent. To relax this assumption, a generalized notion of the reason model has been introduced. While several argumentative explanation approaches exist for reasoning with precedents based on the traditional consistent reason model, there has been no corresponding argumentative explanation method developed for this generalized reasoning framework accommodating inconsistent precedents. To address this question, this paper examines an extension of the derivation state argumentation framework (DSA-framework) to explain the reasoning according to the generalized notion of the reason model.

From Outliers to Topics in Language Models: Anticipating Trends in News Corpora

This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Using vector embeddings from state-of-the-art language models and a cumulative clustering approach, we track their evolution over time in French and English news datasets focused on corporate social responsibility and climate change. The results reveal a consistent pattern: outliers tend to evolve into coherent topics over time across both models and languages.

An action language-based formalisation of an abstract argumentation framework

An abstract argumentation framework is a commonly used formalism to provide a static representation of a dialogue. However, the order of enunciation of the arguments in an argumentative dialogue is very important and can affect the outcome of this dialogue. In this paper, we propose a new framework for modelling abstract argumentation graphs, a model that incorporates the order of enunciation of arguments. By taking this order into account, we have the means to deduce a unique outcome for each dialogue, called an extension. We also establish several properties, such as termination and correctness, and discuss two notions of completeness. In particular, we propose a modification of the previous transformation based on a "last enunciated last updated" strategy, which verifies the second form of completeness.

Action Languages Based Actual Causality in Ethical Decision Making Contexts

Moral responsibility is closely intermixed with causality, even if it cannot be reduced to it. Besides, rationally understanding the evolution of the physical world is inherently linked with the idea of causality. It follows that decision making applications based on automated planning, especially if they integrate references to ethical norms, have inevitably to deal with causality. Despite these considerations, much of the work in computational ethics relegates causality to the background, if not ignores it completely. This paper contribution is double. The first one is to link up two research topics$\unicode{x2014}$automated planning and causality$\unicode{x2014}$by proposing an actual causation definition suitable for action languages. This definition is a formalisation of Wright's NESS test of causation. The second is to link up computational ethics and causality by showing the importance of causality in the simulation of ethical reasoning and by enabling the domain to deal with situations that were previously out of reach thanks to the actual causation definition proposed.