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RESEARCH PRODUCT

A Basic Architecture of an Autonomous Adaptive System With Conscious-Like Function for a Humanoid Robot.

Kenneth J. MackinYasuo Kinouchi

subject

0301 basic medicinebrain-oriented systemComputer sciencelcsh:Mechanical engineering and machinerymedia_common.quotation_subjectlcsh:QA75.5-76.9503 medical and health sciences0302 clinical medicineArtificial IntelligenceAdaptive systemHypothesis and Theorylcsh:TJ1-1570Binding problemAdaptation (computer science)Function (engineering)goal-directed behaviorLibet’s experimentmedia_commonRobotics and AIautonomous adaptationhabitual behaviorArtificial neural networkbusiness.industryComputer Science Applicationsimage processing030104 developmental biologybinding problemRobotlcsh:Electronic computers. Computer scienceArtificial intelligencemodel of consciousnessConsciousnessbusiness030217 neurology & neurosurgeryHumanoid robot

description

In developing a humanoid robot, there are two major objectives. One is developing a physical robot having body, hands, and feet resembling those of human beings and being able to similarly control them. The other is to develop a control system that works similarly to our brain, to feel, think, act, and learn like ours. In this article, an architecture of a control system with a brain-oriented logical structure for the second objective is proposed. The proposed system autonomously adapts to the environment and implements a clearly defined “consciousness” function, through which both habitual behavior and goal-directed behavior are realized. Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is “aware” when making our moment to moment decisions in our daily life. The binding problem and the basic causes of delay in Libet’s experiment are also explained by capturing awareness in this manner. The goal is set as an image in the system, and efficient actions toward achieving this goal are selected in the goal-directed behavior process. The system is designed as an artificial neural network and aims at achieving consistent and efficient system behavior, through the interaction of highly independent neural nodes. The proposed architecture is based on a two-level design. The first level, which we call the “basic-system,” is an artificial neural network system that realizes consciousness, habitual behavior and explains the binding problem. The second level, which we call the “extended-system,” is an artificial neural network system that realizes goal-directed behavior.

10.3389/frobt.2018.00030https://pubmed.ncbi.nlm.nih.gov/33644117