Decision-making in brains and robots - the case for an interdisciplinary approach

作     者：Lee, Sang Wan Seymour, Ben 

作者机构：Korea Adv Inst Sci & Technol Program Brain & Cognit Engn Dept Bio & Brain Engn Daejeon South Korea KAIST Inst Artificial Intelligence Daejeon South Korea KAIST Inst Hlth Sci & Technol Daejeon South Korea Univ Cambridge Dept Engn Computat & Biol Learning Lab Trumpington St Cambridge CB2 1PZ England Adv Telecommun Res Inst Int Brain Informat Commun Res Lab Grp Kyoto Japan Natl Inst Informat & Commun Technol Ctr Informat & Neural Networks 1-4 Yamadaoka Suita Osaka 5650871 Japan 

出 版 物：《CURRENT OPINION IN BEHAVIORAL SCIENCES》 (Curr. Opin. Behav. Sci.)

年 卷 期：2019年第26卷

页      面：137-145页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 04[教育学] 1001[医学-基础医学(可授医学、理学学位)] 

基　　金：Wellcome Trust Arthritis Research UK National Institute of Information and Communications Technology of Japan ICT R&D program of MSIP/IITP [2016-0-00563] Institute for Information & Communications Technology Promotion (IITP) grant - Korea government [2017-0-00451] Institute for Information & communications Technology Promotion (IITP) grant - Korea government (MSIT) [2018-0-00677] Samsung Research Funding Center of Samsung Electronics [SRFC-TC1603-06] KAIST (Korea Advanced Institute of Science and Technology) [G04150045] 

摘      要：Reinforcement Learning describes a general method for trial-and-error learning, and it has emerged as a dominant framework both for optimal control in autonomous robots, and understanding decision-making in the brain. Despite their common roots, however, these two fields have evolved largely independently. In this perspective, we consider how each now face problems that could potentially be addressed by insights from the other, and argue that an interdisciplinary approach could greatly accelerate progress in both.