Contino, GiuseppeGiuseppeContinoCipollone, RobertoRobertoCipolloneFrattolillo, FrancescoFrancescoFrattolilloFanti, AndreaAndreaFantiBrandizzi, NicoloNicoloBrandizziIocchi, LucaLucaIocchi2025-01-302025-01-302024-11-24https://publica.fraunhofer.de/handle/publica/48312510.1145/3687272.36883252-s2.0-85215536463As human-machine interaction contexts become increasingly prevalent, it becomes crucial to identify and formalize the characteristics and parameters influencing trust. This enables the creation of agents capable of inducing higher trust and recognizing whether a partner is trustworthy or not. In this article, we focus on one of the key components affecting trust: competence, the ability to successfully complete a selected task. We evaluate this concept within a Multi-Agent Reinforcement Learning (MARL) framework and introduce the Competence Trust Factor (CTF). Our results demonstrate that incorporating the CTF significantly improves task performance and agent collaboration in various scenarios.enfalseMulti-agent SystemsReinforcement LearningReward MachinesTrust Factorsconference paper