Personalized Agent Explanations for Human-Agent Teamwork: Adapting Explanations to User Trust, Workload, and Performance

Abstract

For human-agent teams to be successful, agent explanations are crucial. These explanations should ideally be personalized by adapting them to intended human users. So far, little work has been conducted on personalized agent explanations during human-agent teamwork. Therefore, an online experiment (n = 60) was conducted to compare personalized agent explanations against a baseline of non-personalized explanations. We implemented four agents who adapted their explanations during a search and rescue task randomly, or based on human workload, performance, or trust. Results show that personalized explanations can increase explanation satisfaction and trust in the agent, but also decrease performance. Therefore, we conclude that personalized agent explanations can be beneficial to human-agent teamwork, but that user modelling and personalization techniques should be carefully considered.