Generation of fuzzy rules and learning algorithms for cooperative behavior of Autonomouse Mobile Robots(AMRs)

Jang Hyun Kim; Jin Bae Park; Hyun Seok Yang; Young Pil Park

doi:10.1007/11539506_126

Generation of fuzzy rules and learning algorithms for cooperative behavior of Autonomouse Mobile Robots(AMRs)

Jang Hyun Kim, Jin Bae Park, Hyun Seok Yang, Young Pil Park

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

Complex "lifelike" behaviors are composed of local interactions of individuals under fundamental rules of artificial life. In this paper, fundamental rules for cooperative group behaviors, "flocking" and "arrangement" of multiple autonomouse mobile robots are represented by a small number of fuzzy rules. Fuzzy rules in Sugeno type and their related parameters are automatically generated from clustering input-output data obtained from the algorithms for the group behaviors. Simulations demonstrate the fuzzy rules successfully realize group intelligence of mobile robots.

Original language	English
Pages (from-to)	1015-1024
Number of pages	10
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	3613
Issue number	PART I
DOIs	https://doi.org/10.1007/11539506_126
Publication status	Published - 2005
Event	Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/11539506_126

Cite this

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Generation of fuzzy rules and learning algorithms for cooperative behavior of Autonomouse Mobile Robots(AMRs). / Kim, Jang Hyun; Park, Jin Bae ; Yang, Hyun Seok et al.
In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3613, No. PART I, 2005, p. 1015-1024.

Research output: Contribution to journal › Conference article › peer-review

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AU - Park, Jin Bae

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AU - Park, Young Pil

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AB - Complex "lifelike" behaviors are composed of local interactions of individuals under fundamental rules of artificial life. In this paper, fundamental rules for cooperative group behaviors, "flocking" and "arrangement" of multiple autonomouse mobile robots are represented by a small number of fuzzy rules. Fuzzy rules in Sugeno type and their related parameters are automatically generated from clustering input-output data obtained from the algorithms for the group behaviors. Simulations demonstrate the fuzzy rules successfully realize group intelligence of mobile robots.

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Generation of fuzzy rules and learning algorithms for cooperative behavior of Autonomouse Mobile Robots(AMRs)

Abstract

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