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Learning to Communicate Proactively in Human-Agent Teaming

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Learning to Communicate Proactively in Human-Agent Teaming

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Samenvatting

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

OrganisatieHogeschool Utrecht
AfdelingKenniscentrum Leren en Innoveren
LectoraatCo-Design
Gepubliceerd inDe La Prieta F. et al. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection. Springer, Cham
Datum2020-07-06
TypeBoekdeel
ISBN978-3-030-51999-5
DOI10.1007/978-3-030-51999-5_20
DOI Communications in Computer and Information Science book series (CCIS, volume 1233)
TaalEngels

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