Article Instance

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{
    "pk": 30456,
    "title": "Building a Computer Model of Learning Classical Mechanics",
    "subtitle": null,
    "abstract": "A computational model of learning in a complex domain is described an its implementation is discussed. The model supports knowledge-based acquisition of problem-solving concepts from observed examples, in the domain of physics problem-solving. The system currently learns aobut momentum conservation, in a psychologically plausible fashion form a background knowledge of Newton's laws and the calculus. In its contribution to machine learning, this research is important for artifical intelligence. From a psychological perspective it demonstrates the computational consistency of a machanism tha tmay underlie human learning iin a complex domain. This work also has implications for computer-adied instruction, in that it advances a  learning model for a complicated domain involving both symbolic and numerical reasoning.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Poster Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/5c04p5q4",
    "frozenauthors": [
        {
            "first_name": "Judea",
            "middle_name": "W.",
            "last_name": "Shavlik",
            "name_suffix": "",
            "institution": "University of Illinois at Urbana-Champaign",
            "department": ""
        },
        {
            "first_name": "Gerald",
            "middle_name": "F.",
            "last_name": "DeJong",
            "name_suffix": "",
            "institution": "",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1985-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/30456/galley/20305/download/"
        }
    ]
}