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• W3040507763 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. 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." @default.
• W3040507763 created "2020-07-10" @default.
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• W3040507763 date "2021-07-13" @default.
• W3040507763 modified "2023-09-23" @default.
• W3040507763 title "Too Many cooks: Bayesian inference for coordinating Multi-agent Collaboration" @default.
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• W3040507763 doi "https://doi.org/10.1093/oso/9780198862536.003.0008" @default.
• W3040507763 hasPublicationYear "2021" @default.
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