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020 ▼a 9781392849491
035 ▼a (MiAaPQ)AAI22619517
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 001
1001 ▼a Pathak, Deepak.
24510 ▼a Learning to Generalize via Self-supervised Prediction.
260 ▼a [S.l.]: ▼b University of California, Berkeley., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 150 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
500 ▼a Advisor: Darrell, Trevor
5021 ▼a Thesis (Ph.D.)--University of California, Berkeley, 2019.
506 ▼a This item must not be sold to any third party vendors.
520 ▼a Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at recognizing objects, cleaning floors, playing complex games and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. Could optimizing towards fixed external goals be hindering the generalization instead of aiding it? In this thesis, we present our initial efforts toward endowing artificial agents with a human-like ability to generalize in diverse scenarios. The main insight is to first allow the agent to learn general-purpose skills in a completely self-directed manner, without optimizing for any external goal.To be able to learn on its own, the claim is that an artificial agent must be embodied in the world, develop an understanding of its sensory input (e.g., image stream) and simultaneously learn to map this understanding to its motor outputs (e.g., torques) in an unsupervised manner. All these considerations lead to two fundamental questions: how to learn rich representations of the world similar to what humans learn?
590 ▼a School code: 0028.
650 4 ▼a Artificial intelligence.
690 ▼a 0800
71020 ▼a University of California, Berkeley. ▼b Computer Science.
7730 ▼t Dissertations Abstracts International ▼g 81-06B.
773 ▼t Dissertation Abstract International
790 ▼a 0028
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493635 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK