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{
    "pk": 50053,
    "title": "A Framework for Modeling Cognitive Processes in Intelligent Agents Using Behavior Trees",
    "subtitle": null,
    "abstract": "Advances in deep multi-agent reinforcement learning learning (MARL) enable sequential decision making for a range of exciting multi-agent applications.\nThe black-box characteristic of MARL restricts the safe and scalable application of decision models in practical deployment.\nHowever, existing interpretability methods for deep reinforcement learning models are are not suitable for addressing challenges posed by multi-agent environments and often inadequate in generating logical sequential decisions.\nWe present an innovative framework called BT4MARL, which introduces the behavior tree structure to explainable MARL.\nThe proposed method clusters state space by aggregating temporally related states and divides agents into several groups in the new state.\nBased on these clustered states and agents, we constructs behavior tree structures.\nIn this way, we use an exploration technique based on pairing a combined behavior tree with the target model.\nWe empirically show that our framework is effective in four benchmark MARL domains. Moreover, the results of a user study show that the generated explanations significantly improve performance and satisfaction.\nThis work represents a significant stride towards addressing the challenges of explainability and performance in MARL applications.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Decision making; Intelligent agents; Machine learning; Knowledge representation"
        }
    ],
    "section": "Abstracts with Poster Presentation (accepted as Abstracts)",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/0532h2sz",
    "frozenauthors": [
        {
            "first_name": "Kejia",
            "middle_name": "",
            "last_name": "Wan",
            "name_suffix": "",
            "institution": "National University of Defense Technology",
            "department": ""
        },
        {
            "first_name": "Yuntao",
            "middle_name": "",
            "last_name": "Liu",
            "name_suffix": "",
            "institution": "Academy of Military Sciences",
            "department": ""
        },
        {
            "first_name": "Hengzhu",
            "middle_name": "",
            "last_name": "Liu",
            "name_suffix": "",
            "institution": "National University of Defense Technology",
            "department": ""
        },
        {
            "first_name": "Xinhai",
            "middle_name": "",
            "last_name": "Xu",
            "name_suffix": "",
            "institution": "Academy of Military Science",
            "department": ""
        },
        {
            "first_name": "Jinlong",
            "middle_name": "",
            "last_name": "Tian",
            "name_suffix": "",
            "institution": "National University of Defense Technology",
            "department": ""
        },
        {
            "first_name": "Xianglong",
            "middle_name": "",
            "last_name": "Li",
            "name_suffix": "",
            "institution": "Academy of Military Sciences",
            "department": ""
        },
        {
            "first_name": "Hao",
            "middle_name": "",
            "last_name": "Tang",
            "name_suffix": "",
            "institution": "NUDT",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2025-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/50053/galley/38015/download/"
        }
    ]
}