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arXiv 2012.07195cs.AI

Efficient Querying for Cooperative Probabilistic Commitments

Artificial Intelligence Institute, University of South Carolina(南卡罗来纳大学人工智能研究所);Computer Science and Engineering, University of Michigan(密歇根大学计算机科学与工程学院)

Qi Zhang, Edmund H. Durfee, Satinder Singh

英文摘要:

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. Agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, should choose a cooperative commitment to maximize their joint reward. We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully-selected commitment choices. We prove structural properties of the agents' values as functions of the parameters of the commitment specification, and develop a greedy method for composing a query with provable approximation bounds, which we empirically show can find nearly optimal commitments in a fraction of the time methods that lack our insights require.

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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