Argumentation-based Agents that Explain their Decisions
  Explainable Artificial Intelligence (XAI) systems, including intelligent
agents, must be able to explain their internal decisions, behaviours and
reasoning that produce their choices to the humans (or other systems) with
which they interact. In this paper, we focus on how an extended model of BDI
(Beliefs-Desires-Intentions) agents can be able to generate explanations about
their reasoning, specifically, about the goals he decides to commit to. Our
proposal is based on argumentation theory, we use arguments to represent the
reasons that lead an agent to make a decision and use argumentation semantics
to determine acceptable arguments (reasons). We propose two types of
explanations: the partial one and the complete one. We apply our proposal to a
scenario of rescue robots.
