Learning to Communicate Proactively in Human-Agent Teaming

Authors Emma van Zoelen, Anita Cremers, Frank Dignum, Jurriaan van Diggelen, Marieke 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

On this publication contributed

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 Context-sensitive, Human-agent teaming, Reinforcement Learning, BDI-agent, Human-agent communication, Proactive
Digital Object Identifier 10.1007/978-3-030-51999-5_20

Anita Cremers

Co-Design