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논문 기본 정보

자료유형
학술저널
저자정보
Sanghoon Lee (한양대학교) Il Hong Suh (한양대학교) Woo Young Kwon (한양대학교)
저널정보
제어로봇시스템학회 International Journal of Control, Automation, and Systems International Journal of Control, Automation, and Systems 제6권 제6호
발행연도
2008.12
수록면
904 - 914 (11page)

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초록· 키워드

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An action-selection-mechanism (ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing-a-box-into-a-goal (PBIG) will be illustrated.

목차

Abstract
1. INTRODUCTION
2. ACTION SELECTION MECHANISM
3. SHORTEST PATH-BASED REINFORCEMENT LEARNING OF DBM
4. COMPARISONS WITH OTHER ASMS
5. EXPERIMENTAL RESULTS
6. CONCLUSIONS
REFERENCES

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UCI(KEPA) : I410-ECN-0101-2013-569-003217793