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{ "pk": 27244, "title": "Geometric Concept Acquisition in a Dueling Deep Q-Network", "subtitle": null, "abstract": "Explaining how intelligent systems come to embody knowl-edge of deductive concepts through inductive learning is afundamental challenge of both cognitive science and artificialintelligence. We address this challenge by exploring how adeep reinforcement learning agent, occupying a setting simi-lar to those encountered by early-stage mathematical conceptlearners, comes to represent ideas such as rotation and trans-lation. We first train a Dueling Deep Q-Network on a shapesorting task requiring implicit knowledge of geometric proper-ties, then we query this network with classification and prefer-ence selection tasks. We demonstrate that scalar reinforcementprovides sufficient signal to learn representations of shape cat-egories. After training, the model shows a preference for moresymmetric shapes, which it can sort more quickly than lesssymmetric shapes, supporting the view symmetry preferencesmay be acquired from goal-directed experience.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "Posters: Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/3v11228x", "frozenauthors": [ { "first_name": "Alex", "middle_name": "", "last_name": "Kuefler", "name_suffix": "", "institution": "Stanford University", "department": "" }, { "first_name": "Mykel", "middle_name": "J.", "last_name": "Kochenderfer", "name_suffix": "", "institution": "Stanford University", "department": "" }, { "first_name": "James", "middle_name": "L.", "last_name": "McClelland", "name_suffix": "", "institution": "Stanford University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2017-01-01T13:00:00-05:00", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27244/galley/16880/download/" } ] }