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{
    "pk": 27201,
    "title": "Beyond Almost-Sure Termination",
    "subtitle": null,
    "abstract": "The aim of this paper is to argue that models in cognitivescience based on probabilistic computation should not be re-stricted to those procedures that almost surely (with probabil-ity 1) terminate. There are several reasons to consider non-terminating procedures as candidate components of cognitivemodels. One theoretical reason is that there is a perfect cor-respondence between the enumerable semi-measures and allprobabilistic programs, as we demonstrate here (generalizinga better-known fact about computable measures and almost-surely halting programs). One practical reason is that the linebetween almost sure termination and non-termination is elu-sive, as well as arbitrary. We argue that this matters not onlyfor theorists, but also potentially for a learner faced with thetask of inducing programs from experience.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Posters: Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/5tp978xp",
    "frozenauthors": [
        {
            "first_name": "Thomas",
            "middle_name": "F.",
            "last_name": "Icard",
            "name_suffix": "",
            "institution": "Stanford University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2017-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27201/galley/16837/download/"
        }
    ]
}