论文标题
协调的自我解释偏差
Self-Explaining Deviations for Coordination
论文作者
论文摘要
在现实世界中，完全合作，部分可观察到的多代理问题无处不在。在本文中，我们关注的是一个特定的协调问题子类，其中人类能够发现自我解释偏差（SED）。 SED是偏离对正常情况下哪种合理行为的共同理解的行动。它们的目的是使其他代理人或其他代理人使用心理理论意识到这种情况必须是异常的。我们首先以现实世界的榜样激励SED并将其定义形式化。接下来，我们介绍了一种新型算法，改进，最大程度地提高了自我解释偏差（即兴），以执行SED。最后，我们在说明性的玩具环境和流行的基准设置哈纳比（Hanabi）中评估了即兴创作，这是第一个生产所谓的精美作品的方法，这些方法被认为是人类心理理论的更具标志性的例子之一。
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.
