Work

Modeling a Trust Factor in Composite Tasks for Multi-Agent Reinforcement Learning

Year: 2024

Type: conference-paper

Abstract: As 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.

Source: International Conference on Human-Agent Interaction (HAI)

DOI: 10.1145/3687272.3688325

Authors: Giuseppe Contino, Roberto Cipollone, Francesco Frattolillo, Andrea Fanti, Nicolo' Brandizzi, Luca Iocchi

Institution: Sapienza University of Rome

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Universitas Brawijaya

Award: G4615704299