Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

No abstract

No abstract

We present a generalisation of the Event Calculus, specified in classical logic and implemented in ASP, that facilitates reasoning about non-binary-valued fluents in domains with non-deterministic, triggered, concurrent, and possibly conflicting actions. We show via a case study how this framework may be used as a basis for a "possible-worlds" style approach to epistemic and causal reasoning in a narrative setting. In this framework an agent may gain knowledge about both fluent values and action occurrences through sensing actions, lose knowledge via non-deterministic actions, and represent plans that include conditional actions whose conditions may be initially unknown.

No abstract