Assessing and understanding intelligent agents is a difficult task for users who lack an AI background. “Explainable AI” (XAI) aims to address this problem, but what should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this article, we present a series of studies 1 using IFT: the first investigates how expert explainers supply explanations in the RTS domain, the second investigates what explanations domain experts demand from agents in the RTS domain, and the last focuses on how both populations try to explain a state-of-the-art AI. Our results show that RTS environments like StarCraft offer so many options that change so rapidly, foraging tends to be very costly. Ways foragers attempted to manage such costs included “satisficing” approaches to reduce their cognitive load, such as focusing more on What information than on Why information, strategic use of language to communicate a lot of nuanced information in a few words, and optimizing their environment when possible to make their most valuable information patches readily available. Further, when a real AI entered the picture, even very experienced domain experts had difficulty understanding and judging some of the AI’s unconventional behaviors. Finally, our results reveal ways Information Foraging Theory can inform future XAI interactive explanation environments, and also how XAI can inform IFT.

中文翻译：

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喊话者、游戏爱好者和人工智能：寻找即时战略玩家的解释

对于缺乏人工智能背景的用户来说，评估和理解智能代理是一项艰巨的任务。“可解释的人工智能”（XAI）旨在解决这个问题，但解释中应该包含什么？回答这个问题的一个途径是转向关于人类如何试图获取他们所寻求的信息的理论。信息觅食理论（IFT）就是这样一种理论。在本文中，我们介绍了一系列研究1使用 IFT：第一个调查专家解释器如何供应RTS领域的解释，第二个调查领域专家的解释是什么要求来自 RTS 领域的代理，最后一个重点是两个群体如何试图解释最先进的人工智能。我们的研究结果表明，像星际争霸这样的 RTS 环境提供了如此多变化如此之快的选项，觅食往往非常昂贵。觅食者试图管理这些成本的方法包括“满足”的方法来减少他们的认知负荷，例如更多地关注什么信息而不是为什么信息、战略性地使用语言用几句话来传达大量细微的信息，以及优化他们的环境尽可能让他们最有价值的信息补丁随时可用。此外，当真正的人工智能进入画面时，即使是非常有经验的领域专家也难以理解和判断人工智能的一些非常规行为。最后，