Understanding human intention by connecting perception and action learning in artificial agents

Neural Networks

Volumn 92, Issue , 2017, Pages 29-38

  Kim, Sangwook ^a   Yu, Zhibin ^b   Lee, Minho ^a  

^a Kyungpook National University   (South Korea)

^b OCEAN UNIVERSITY OF CHINA   (China)

Author keywords

Affordance; Cognitive agent; Human robot interaction; Intention understanding; Object Augmented Multiple Timescale Recurrent Neural Network; Perception action connected learning

Indexed keywords

DEEP NEURAL NETWORKS; MAN MACHINE SYSTEMS; NEUROLOGY; RECURRENT NEURAL NETWORKS; ROBOTS;

AFFORDANCES; COGNITIVE AGENTS; INTENTION UNDERSTANDING; MULTIPLE TIMESCALE RECURRENT NEURAL NETWORKS; PERCEPTION-ACTION;

HUMAN ROBOT INTERACTION;

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BEHAVIOR; COGNITION; COMPUTER MODEL; HUMAN; HUMAN COMPUTER INTERACTION; MEASUREMENT ACCURACY; MENTAL DEVELOPMENT; MENTAL TASK; OBJECT AUGMENTED SUPERVISED MULTIPLE TIMESCALE RECURRENT NEURAL NETWORK; PERCEPTION; PRIORITY JOURNAL; RECOGNITION; BIOMETRY; BODY POSITION; MOVEMENT (PHYSIOLOGY); PROCEDURES; SUPPORT VECTOR MACHINE;

BIOMETRIC IDENTIFICATION; HUMANS; INTENTION; MOVEMENT; NEURAL NETWORKS (COMPUTER); POSTURE; SUPPORT VECTOR MACHINE;

EID: 85015022239     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2017.01.009     Document Type: Article

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