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{
    "pk": 29495,
    "title": "Too many cooks: Coordinating multi-agent collaboration through inverse planning",
    "subtitle": null,
    "abstract": "Collaboration requires agents to coordinate their behavior onthe fly, sometimes cooperating to solve a single task togetherand other times dividing it up into sub-tasks to work on in par-allel. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that driveothers to act. Here, we develop Bayesian Delegation, a decen-tralized multi-agent learning mechanism with these abilities.Bayesian Delegation enables agents to rapidly infer the hid-den intentions of others by inverse planning. These inferencesenable agents to flexibly decide in the absence of communi-cation when to cooperate on the same sub-task and when towork on different sub-tasks in parallel. We test this model ina suite of multi-agent Markov decision processes inspired bycooking problems. To succeed, agents must coordinate boththeir high-level plans (e.g., what sub-task they should work on)and their low-level actions (e.g., avoiding collisions). BayesianDelegation bridges these two levels and rapidly aligns agents’beliefs about who should work on what. Finally, we testedBayesian Delegation in a behavioral experiment where partici-pants made sub-task inferences from sparse observations of co-operative behavior. Bayesian Delegation outperformed heuris-tic models and was closely aligned with human judgments.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "coordination; social learning; inverse planning;Bayesian inference"
        }
    ],
    "section": "Agend-based Models",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/7zk0m8cz",
    "frozenauthors": [
        {
            "first_name": "Sarah",
            "middle_name": "A.",
            "last_name": "Wu",
            "name_suffix": "",
            "institution": "MIT",
            "department": ""
        },
        {
            "first_name": "Rose",
            "middle_name": "E.",
            "last_name": "Wang",
            "name_suffix": "",
            "institution": "MIT",
            "department": ""
        },
        {
            "first_name": "James",
            "middle_name": "A.",
            "last_name": "Evans",
            "name_suffix": "",
            "institution": "UChicago",
            "department": ""
        },
        {
            "first_name": "Joshua",
            "middle_name": "B.",
            "last_name": "Tenenbaum",
            "name_suffix": "",
            "institution": "MIT",
            "department": ""
        },
        {
            "first_name": "David",
            "middle_name": "C.",
            "last_name": "Parkes",
            "name_suffix": "",
            "institution": "Harvard",
            "department": ""
        },
        {
            "first_name": "Max",
            "middle_name": "",
            "last_name": "Kleiman-Weiner",
            "name_suffix": "",
            "institution": "Harvard, MIT , Diffeo",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2020-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/29495/galley/19355/download/"
        }
    ]
}