59 citations · 194 across the 46 of their papers we have counts for
9 papers · 1 filter
Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks
Marco Matarese, Francesco Rea, Katharina J. Rohlfing +1
Collaborative decision-making with artificial intelligence (AI) agents presents opportunities and challenges. While human-AI performance often surpasses that of individuals, the im…
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation
Iveta Bečková, Štefan Pócoš, Giulia Belgiovine +4
The addressee estimation (understanding to whom somebody is talking) is a fundamental task for human activity recognition in multi-party conversation scenarios. Specifically, in th…
How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations
Marco Matarese, Francesco Rea, Alessandra Sciutti
There is an increasing consensus about the effectiveness of user-centred approaches in the explainable artificial intelligence (XAI) field. Indeed, the number and complexity of per…
All by Myself: Learning Individualized Competitive Behaviour with a Contrastive Reinforcement Learning optimization
Pablo Barros, Alessandra Sciutti
In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. Besides dealing with the i…
Incorporating Rivalry in Reinforcement Learning for a Competitive Game
Pablo Barros, Ozge Nilay Yalcın, Ana Tanevska +1
Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on specific interaction tasks. However, most interactive sc…
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction
Marco Matarese, Francesco Rea, Alessandra Sciutti
State of the art Artificial Intelligence (AI) techniques have reached an impressive complexity. Consequently, researchers are discovering more and more methods to use them in real-…