Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which humans are able to discover self-explaining deviations (SEDs). SEDs are actions that deviate from the common understanding of what reasonable behavior would be in normal circumstances. They are taken with the intention of causing another agent or other agents to realize, using theory of mind, that the circumstance must be abnormal. We first motivate SED with a real world example and formalize its definition. Next, we introduce a novel algorithm, improvement maximizing self-explaining deviations (IMPROVISED), to perform SEDs. Lastly, we evaluate IMPROVISED both in an illustrative toy setting and the popular benchmark setting Hanabi, where it is the first method to produce so called finesse plays, which are regarded as one of the more iconic examples of human theory of mind.

本文研究合作多智能体问题的一种特定子类，即人类能够发现自我解释偏差（SED）。作者首先给出了SED的定义，然后提出了一种新算法IMPROVISED来执行SED，并在Hanabi基准设置中进行了评估。IMPROVISED是第一种能够产生所谓的优雅玩法的方法，这被认为是人类心理理论的更具象征性的例子之一。

协调机制的自说明偏差