Learning to Communicate Proactively in Human-Agent Teaming

Auteurs Emma van Zoelen , Anita Cremers , Frank Dignum , Jurriaan van Diggelen , Marieke M. Peeters
Gepubliceerd in De La Prieta F. et al. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection.
Publicatiedatum 6 juli 2020
Lectoraat Co-Design
Soort publicatie Boek

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

Aan deze publicatie werkten mee

Taal Engels
Gepubliceerd 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
Trefwoorden Context-sensitive, Human-agent teaming, Reinforcement Learning, BDI-agent, Human-agent communication, Proactive
Digital Object Identifier https://doi.org/10.1007/978-3-030-51999-5_20

Anita Cremers

Co-Design