Article Instance

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{
    "pk": 32822,
    "title": "Adaptive Action Selection",
    "subtitle": null,
    "abstract": "In earlier papers we presented a distributed model of action selection in an autonomous intelligent agent (Maes, 1989a, 1989b, 1991a, 1991b). An interesting feature of this algorithm is that it provides a handful of parameters that can be used to tune the action selection behavior of the algorithm. They make it possible, for example, to trade off goal-orientedness for data-orientedness, speed for quality, bias (inertia) for adaptivity, and so on. In this paper we report on an experiment we did in automating the tuning and run-time adaptation of these parameters. The same action selection model is used on a meta-level to select actions that alter the values of the parameters, so as to achieve the action selection behavior that is appropriate for the environment and task at hand.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Paper Presentations -- Planning and Action",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/650020zd",
    "frozenauthors": [
        {
            "first_name": "Pattie",
            "middle_name": "",
            "last_name": "Maes",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1991-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/32822/galley/23882/download/"
        }
    ]
}