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Argumentation and Causal Models in Human-Machine Interaction: A Round Trip

Abstract

In the field of explainable artificial intelligence (XAI), causal models and abstract argumentation frameworks are two formal approaches that provide a definition of an explanation. These symbolic approaches rely on logical formalisms to reason by abduction or to search for causalities, from the formal modeling of a problem or a situation. These models are designed to satisfy a number of properties found in explanations given by one human to another which are particularly interesting for humanmachine interactions as well. In this paper, we show the equivalence between a particular type of causal models, that we call argumentative causal graphs (ACG), and abstract argumentation frameworks. We also propose a transformation between these two systems and look at how one definition of an explanation in the argumentation theory is transposed when moving to ACG. To illustrate our proposition, we use a very simplified version of a screening agent for COVID-19.
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Dates and versions

hal-03739310 , version 1 (27-07-2022)

Identifiers

  • HAL Id : hal-03739310 , version 1

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Yann Munro, Isabelle Bloch, Mohamed Chetouani, Marie-Jeanne Lesot, Catherine Pelachaud. Argumentation and Causal Models in Human-Machine Interaction: A Round Trip. 8th International Workshop on Artificial Intelligence and Cognition, Jun 2022, Örebro, Sweden. ⟨hal-03739310⟩
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