In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language that allows us to model the evolution of the world and to establish causal relationships between the enunciation of arguments and their consequences on the acceptability of other arguments, whether direct or indirect. An Answer Set Programming implementation is also described. Furthermore, we lay the ground for the generation of explanations using the causal relations as well as two graphical representations to support such explanations.

In the Knowledge Representation and Reasoning field, causality has mainly been used to determine the effects of actions. However, in legal or ethical reasoning, causality is used to determine the causal origin of a consequence. Such a posteriori reasoning is the focus of the Actual Causality field which has been extensively studied by lawyers, philosophers, mathematicians, and computer scientists. While most of the situations are easy to solve, there are several overdetermination cases that are far from trivial and that are still a source of disagreements in the field. Recent works have undertaken to adapt causality results into languages for Reasoning about Action and Change (RAC). In this paper, we aim to provide means to effectively address overdetermination issues to researchers seeking to incorporate actual causality into their RAC methods. To do so, we propose a definition of overdetermination in a Labelled Transition System and we formalise and enrich the existing typology of classical cases of overdetermination within this formalisation. The formal typology obtained enables the description of axiomatic properties of RAC methods that deal with overdetermination. To illustrate this, we describe the properties of a recent RAC formalisation of causality in light of this typology. We think that this way of doing can be generalised to all causality RAC formalisations.

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.

Résumé Dans le cadre de l’argumentation abstraite, nous pré-sentons les bénéfices de prendre en compte la temporalité, c’est-à-dire l’ordre d’énonciation des arguments, ainsi que la causalité. Nous proposons une réécriture des graphes d’argu-mentation abstraits acycliques dans un langage d’action per-mettant de modéliser l’évolution du monde et d’établir des relations causales entre l’énonciation des arguments et leurs conséquences directes comme indirectes. Une implémenta-tion en Answer Set Programming est également proposée ainsi que des perspectives pour aller vers des explications. Abstract In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language, that allows us to model the evolution of the world, and to establish causal relationships between the enunciation of arguments and their consequences, whether direct or indirect. An Answer Set Programming implementation is also proposed, as well as perspectives towards explanations.

Rationally understanding the evolution of the physical world is inherently linked with the idea of causality. It follows that agents based on automated planning have inevitably to deal with causality, especially when considering imputability. However, the many debates around causation in the last decades have shown how complex this notion is and thus, how difficult it is to integrate it with planning. This paper's contribution is to link up two research topics-automated planning and causality-by proposing an actual causation definition suitable for action languages. This definition is a formalisation of Wright's NESS test of causation.

Although moral responsibility is not circumscribed by causality, they are both closely intermixed. Furthermore, rationally understanding the evolution of the physical world is inherently linked with the idea of causality. Thus, the decision-making applications based on automated planning inevitably have to deal with causality, especially if they consider imputability aspects or integrate references to ethical norms. The many debates around causation in the last decades have shown how complex this notion is and thus, how difficult is its integration with planning. As a result, much of the work in computational ethics relegates causality to the background, despite the considerations stated above. This paper's contribution is to provide a complete and sound translation into logic programming from an actual causation definition suitable for action languages, this definition is a formalisation of Wright's NESS test. The obtained logic program allows to deal with complex causal relations. In addition to enabling agents to reason about causality, this contribution specifically enables the computational ethics domain to handle situations that were previously out of reach. In a context where ethical considerations in decision-making are increasingly important, advances in computational ethics can greatly benefit the entire AI community.

This paper presents the ACE modular framework (Action-Causality-Ethics), a modular and declarative logic-based framework for representing and applying multiple ethical principles. We clearly separate modeling concerns about dynamics and ethics, using reifica- tion to explicitly represent generic meta-rules to be con-trasted with specific domain knowledge. This allows us to formalize ethical principles as methods for ethical assessment of actions and plans and specify what addi- tional domain information are needed for each of them in addition to the factual description of the unfolding of events. The architecture presented here is based on an action model allowing concurrency and multiple agents and we discuss how this a ff ects the ethical assessment process and allows precise modelling of omission.