{"pk":29962,"title":"Analogy as Nonparametric Bayesian Inference over Relational Systems","subtitle":null,"abstract":"Much of human learning and inference can be framed withinthe computational problem of relational generalization. Inthis project, we propose a Bayesian model that generalizesrelational knowledge to novel environments by analogicallyweighting predictions from previously encountered relationalstructures. First, we show that this learner outperforms anaive, theory-based learner on relational data derived fromrandom- and Wikipedia-based systems when experience withthe environment is small. Next, we show how our formal-ization of analogical similarity translates to the selection andweighting of analogies. Finally, we combine the analogy-and theory-based learners in a single nonparametric Bayesianmodel, and show that optimal relational generalizationtransitions from relying on analogies to building a theory ofthe novel system with increasing experience in it. Beyondpredicting unobserved interactions better than either baseline,this formalization gives a computational-level perspective onthe formation and abstraction of analogies themselves.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"generalization; inference; analogy; Bayesianmodels"},{"word":"nonparametric statistics"}],"section":"Poster Session 3","is_remote":true,"remote_url":"https://escholarship.org/uc/item/86j8j93w","frozenauthors":[{"first_name":"Ruairidh","middle_name":"M.","last_name":"Battleday","name_suffix":"","institution":"Princeton University","department":""},{"first_name":"Thomas","middle_name":"L.","last_name":"Griffiths","name_suffix":"","institution":"Princeton University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29962/galley/19816/download/"}]}