The quest of parsimonious XAI: A human-agent architecture for explanation formulation

by Yazan Mualla and Igor Tchappi and Timotheus Kampik and Amro Najjar and Davide Calvaresi and Abdeljalil Abbas-Turki and Stéphane Galland and Christophe Nicolle Abstract With the widespread use of Artificial Intelligence (AI), understanding the behavior of intelligent agents and robots is crucial to guarantee successful human-agent collaboration since it is not straightforward for humans to understand an agent’s state of mind. Recent empirical studies have confirmed that explaining a system’s behavior to human users fosters the latter’s acceptance of the system.
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Explanation-Based Negotiation Protocol for Nutrition Virtual Coaching

by Berk Buzcu, Vanitha Varadhajaran, Igor Tchappi, Amro Najjar, Davide Calvaresi and Reyhan Aydoğan Abstract People’s awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems.
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Metrics for Evaluating Explainable Recommender Systems

by Joris Hulstijn, Igor Tchappi, Amro Najjar, and Reyhan Aydoğan Abstract Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations.
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