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Computer Science > Artificial Intelligence

arXiv:2207.12322 (cs)
[Submitted on 13 Jul 2022]

Title:Self-Explaining Deviations for Coordination

Authors:Hengyuan Hu, Samuel Sokota, David Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob N. Foerster
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Abstract: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.
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2207.12322 [cs.AI]
  (or arXiv:2207.12322v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2207.12322

Submission history

From: Hengyuan Hu [view email]
[v1] Wed, 13 Jul 2022 20:56:59 UTC (1,185 KB)
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