Article Instance

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{
    "pk": 30769,
    "title": "Integrating Generalizations with Exemplar-Based Reasoning",
    "subtitle": null,
    "abstract": "Knowledge represented as generalizations is insufficient for problem solving in many domains,such as legal reasoning, because of a gap between the language of case-descriptions and the language in which generalizations are expressed, and because of the graded structure of domain categories. Exemplar-based representation addresses these problems, but accurate assessment of similarity between an exemplar of a category and a new case requires reasoning both with general domain theory and with the explanation of the exemplar's membership in the category. G R E B E is a system that integrates generalizations and exemplars in a cooperative manner. Exemplar-based explanations are used to bridge the gap between case-descriptions and generalizations, and domain theory in the form of general rules and specific explanations is used to explain the equivalence of new cases to exemplars.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Paper Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/5hw6s0kn",
    "frozenauthors": [
        {
            "first_name": "L.",
            "middle_name": "Karl",
            "last_name": "Branting",
            "name_suffix": "",
            "institution": "University of Texas",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1989-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
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            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/30769/galley/20618/download/"
        }
    ]
}