Too many cooks: Bayesian inference for coordinating multi-agent collaboration

• URL: http://arxiv.org/abs/2003.11778v2
• Date: Mon, 6 Jul 2020 00:59:13 GMT
• Title: Too many cooks: Bayesian inference for coordinating multi-agent collaboration
• Authors: Rose E. Wang, Sarah A. Wu, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, Max Kleiman-Weiner
• Abstract summary: Collaboration requires agents to coordinate their behavior on the fly. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act. We develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities.
• Score: 55.330547895131986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub-tasks to work on in parallel. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi-agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high-level plans (e.g. what sub-task they should work on) and their low-level actions (e.g. avoiding getting in each other's way). In a self-play evaluation, Bayesian Delegation outperforms alternative algorithms. Bayesian Delegation is also a capable ad-hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results demonstrate the power of Bayesian Delegation for decentralized multi-agent collaboration.

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