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{ "pk": 29733, "title": "A Model of Fast Concept Inference with Object-Factorized Cognitive Programs", "subtitle": null, "abstract": "The ability of humans to quickly identify general conceptsfrom a handful of images has proven difficult to emulate withrobots. Recently, a computer architecture was developed thatallows robots to mimic some aspects of this human ability bymodeling concepts as cognitive programs using an instructionset of primitive cognitive functions. This allowed a robot toemulate human imagination by simulating candidate programsin a world model before generalizing to the physical world.However, this model used a naive search algorithm that re-quired 30 minutes to discover a single concept, and becameintractable for programs with more than 20 instructions. Tocircumvents this bottleneck, we present an algorithm that emu-lates the human cognitive heuristics of object factorization andsub-goaling, allowing human-level inference speed, improvingaccuracy, and making the output more explainable.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "zero-shot; cognitive programs; program induc-tion; concept inference; imitation learning" } ], "section": "Poster Session 2", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/0zq2m13d", "frozenauthors": [ { "first_name": "Anonymous CogSci submission", "middle_name": "", "last_name": "", "name_suffix": "", "institution": "", "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/29733/galley/19590/download/" } ] }