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
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In deze bijdrage wordt verslag gedaan van de afstudeertafels van het CBSS 2020 experiment, waar 29 studenten communicatie en international communication en 5 studenten van de Academie Minerva in deelnamen
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Empathy competence is considered a key aspect of excellent performancein communication professions. But we lack an overview of the specificknowledge, attitudes, and skills required to develop such competence inprofessional communication. Through interviews with 35 seasoned communication professionals, this article explores the role and nature ofempathy competence in professional interactions. The analysis resulted in aframework that details the skills, knowledge, and attitudinal aspects ofempathy; distinguishes five actions through which empathy manifests itself;and sketches relationships of empathy with several auxiliary factors. Theframework can be used for professional development, recruitment, and thedesign of communication education programs.
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