How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games
This work addresses the challenge of making AI explanations accessible to ordinary users, which is incremental as it builds on existing research in explainable AI by drawing from human expertise.
The study tackled the problem of explaining AI agent behaviors to non-expert users by analyzing human shoutcasters in real-time strategy games, revealing insights into their information foraging strategies and explanation patterns.
How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area, for if an AI-based explanation system could effectively explain intelligent agents' behavior, it could enable the end users to understand, assess, and appropriately trust (or distrust) the agents attempting to help them. To provide insights into this question, we turned to human expert explainers in the real-time strategy domain, "shoutcaster", to understand (1) how they foraged in an evolving strategy game in real time, (2) how they assessed the players' behaviors, and (3) how they constructed pertinent and timely explanations out of their insights and delivered them to their audience. The results provided insights into shoutcasters' foraging strategies for gleaning information necessary to assess and explain the players; a characterization of the types of implicit questions shoutcasters answered; and implications for creating explanations by using the patterns