Learning to communicate proactively in human-agent teaming
van Zoelen, E.M.
van Diggelen, J.
De La Prieta, F. (editor)
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. © Springer Nature Switzerland AG 2020.
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18th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2020; L’Aquila; Italy; 7 October 2020 through 9 October 2020, 1233, 238-249
Communications in Computer and Information Science