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{
    "pk": 32064,
    "title": "Bottom-up Skill Learning in Reactive Sequential Decision Tasks",
    "subtitle": null,
    "abstract": "This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent's experience interacting with the world. Computational experiments with the model in two domains are reported.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Posters",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/5d59m60n",
    "frozenauthors": [
        {
            "first_name": "Ron",
            "middle_name": "",
            "last_name": "Sun",
            "name_suffix": "",
            "institution": "The University of Alabama",
            "department": ""
        },
        {
            "first_name": "Todd",
            "middle_name": "",
            "last_name": "Peterson",
            "name_suffix": "",
            "institution": "The University of Alabama",
            "department": ""
        },
        {
            "first_name": "Edward",
            "middle_name": "",
            "last_name": "Merrill",
            "name_suffix": "",
            "institution": "The University of Alabama",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1996-01-01T10:00:00-08:00",
    "render_galley": null,
    "galleys": [
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            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/32064/galley/23129/download/"
        }
    ]
}