NDLI: Learning of action patterns and reactive behavior plans via a novel two-layered ethology-based action selection mechanism

Content Provider	IEEE Xplore Digital Library
Author	II Hong Suh Sanghoon Lee Woo Young Kwon Young-Jo Cho
Copyright Year	2005
Description	Author affiliation: Graduate Sch. of Inf. & Commun., Hanyang Univ., Seoul, South Korea (II Hong Suh; Sanghoon Lee)
Abstract	The two most important abilities for a robot to survive in a given environment are selecting and learning the most appropriate actions in a given situation. Historically, they have also been the biggest problems in robotics. To help solve this problem, we propose a two-layered action selection mechanism (ASM) which designates an action pattern layer and a reactive behavior plan layer. In the reactive behavior plan layer, a task is selected by comparing behavior motivation values that, in an animal, correspond to external stimuli as well as internal states due to hormones. After a task is selected, its corresponding reactive behavior plan is executed as a set of sequential dynamic behavior motivations (DBMs), each of which is associated with an action pattern. In the action pattern layer, each action pattern can be functionally decomposed into primitive motor actions. Shortest path-based Q-learning (SPQL) is incorporated into both the reactive behavior plan and action pattern layers. In the reactive behavior plan layer, relationships between perceptions and action patterns are learned to satisfy a given motivation, as well as the relative priorities of these relationships. In the action pattern layer, the relations between sensory states and primitive motor actions can be learned. To establish the validity of our proposed ASM, experiments with our real designed robot was illustrated together with simulations.
Starting Page	1799
Ending Page	1805
File Size	255707
Page Count	7
File Format	PDF
ISBN	0780389123
DOI	10.1109/IROS.2005.1545148
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot kinematics Robot sensing systems Animals Learning Artificial intelligence Biochemistry Testing Ethology Action Selection Mechanism Reinforcement Learning
Content Type	Text
Resource Type	Article

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