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Learning to Communicate Proactively in Human-Agent Teaming

Part of the Communications in Computer and Information Science book series (CCIS,volume 1233)

Abstract

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.

Keywords

  • Human-agent teaming
  • Reinforcement Learning
  • BDI-agent
  • Human-agent communication
  • Proactive
  • Context-sensitive

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Correspondence to Emma M. van Zoelen .

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van Zoelen, E.M., Cremers, A., Dignum, F.P.M., van Diggelen, J., Peeters, M.M. (2020). Learning to Communicate Proactively in Human-Agent Teaming. In: , et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection. PAAMS 2020. Communications in Computer and Information Science, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51999-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-51999-5_20

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