{"pk":28555,"title":"Model-based Approach with ACT-Rabout Benefits of Memory-based Strategy on Anomalous Behaviors","subtitle":null,"abstract":"Users sometimes face anomalous behaviors of systems, such asmachine failures and autonomous agents. Predicting suchbehaviors of systems is difficult. We investigate the benefits ofthe memory-based strategy, which focuses on memorization ofinstances to predict anomalous and regular behaviors of thesystem, with ACT-R simulations with a cognitive model. Inthis study, we presumed the parameters defining the encodingprocesses on anomalous instances and regular instances in themodel of the memory-based strategy and performedsimulations to verify how these two parameters influenceprediction performance. The results of simulations showed that(1) regular instances are not encoded as default values in thememory-based strategy and that (2) such inactivity on regularinstances suppresses commission errors of regular instancesand does not suppress commission errors of anomalousinstances nor omission errors.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"memory-based strategy; prediction; anomalousbehavior; regular behavior; ACT-R"}],"section":"Papers with Oral Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/4kk5q08w","frozenauthors":[{"first_name":"Shota","middle_name":"","last_name":"Matsubayashi","name_suffix":"","institution":"Nagoya University","department":""},{"first_name":"Kazuhisa","middle_name":"","last_name":"Miwa","name_suffix":"","institution":"Kindai University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2019-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28555/galley/18426/download/"}]}