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