arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.09318cs.MAcs.AI
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Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors

DeepMind(深度思维);University of California Berkeley(加州大学伯克利分校);Center for Human-Compatible AI(人类兼容人工智能中心);Schwartz Reisman Institute for Technology and Society(施瓦茨·赖斯曼技术与社会研究所);University of Toronto(多伦多大学);Vector Institute(向量研究所);OpenAI(开放人工智能研究中心)

Raphael Köster, Dylan Hadfield-Menell, Gillian K. Hadfield, Joel Z. Leibo

英文摘要:

How can societies learn to enforce and comply with social norms? Here we investigate the learning dynamics and emergence of compliance and enforcement of social norms in a foraging game, implemented in a multi-agent reinforcement learning setting. In this spatiotemporally extended game, individuals are incentivized to implement complex berry-foraging policies and punish transgressions against social taboos covering specific berry types. We show that agents benefit when eating poisonous berries is taboo, meaning the behavior is punished by other agents, as this helps overcome a credit-assignment problem in discovering delayed health effects. Critically, however, we also show that introducing an additional taboo, which results in punishment for eating a harmless berry, improves the rate and stability with which agents learn to punish taboo violations and comply with taboos. Counterintuitively, our results show that an arbitrary taboo (a "silly rule") can enhance social learning dynamics and achieve better outcomes in the middle stages of learning. We discuss the results in the context of studying normativity as a group-level emergent phenomenon.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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