{"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/"}]}