Learning to Communicate Proactively in Human-Agent Teaming

Authors Emma van Zoelen , Dr. Anita Cremers , Frank Dignum , Jurriaan van Diggelen , Marieke M. Peeters
Published in De La Prieta F. et al. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection.
Publication date 6 July 2020
Research groups Co-design
Type Book

Summary

Artificially intelligent agents increasingly collaborate with humans in human-agent teams. Timely proactive sharing of relevant information within the team contributes to the overall team performance. This paper presents a machine learning approach to proactive communication in AI-agents using contextual factors. Proactive communication was learned in two consecutive experimental steps: (a) multi-agent team simulations to learn effective communicative behaviors, and (b) human-agent team experiments to refine communication suitable for a human team member. Results consist of proactive communication policies for communicating both beliefs and goals within human-agent teams. Agents learned to use minimal communication to improve team performance in simulation, while they learned more specific socially desirable behaviors in the human-agent team experiment

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Language English
Published in De La Prieta F. et al. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection.
ISBN/ISSN URN:ISBN:978-3-030-51999-5
Key words Human-agent teaming, Reinforcement Learning, BDI-agent, Human-agent communication, Proactive, Context-sensitive
Digital Object Identifier https://doi.org/10.1007/978-3-030-51999-5_20