Autonomous strategy determination with learning of environments in multi-agent continuous cleaning

Ayumi Sugiyama, Toshiharu Sugawara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

With the development of robot technology, we can expect selfpropelled robots working in large areas where coordinated and collaborative behaviors by multiple robots are necessary. Thus, the learning appropriate strategy for coordination and cooperation in multiple autonomous agents is an important issue. However, conventional methods assumed that agents was given knowledge about the environment. This paper proposes a method of autonomous strategy learning for multiple agents coordination integrated with learning where are easy to become dirty in the environments using examples of continuous cleaning tasks. We found that agents with the proposed method could operate as effectively as those with the conventional method and we found that the proposed method often outperformed it in complex areas by splitting up in their works.

Original languageEnglish
Title of host publicationPRIMA 2014
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 17th International Conference, Proceedings
EditorsHoa Khanh Dam, Jeremy Pitt, Yang Xu, Guido Governatori, Takayuki Ito
PublisherSpringer Verlag
Pages455-462
Number of pages8
ISBN (Electronic)9783319131900
DOIs
Publication statusPublished - 2014 Jan 1
Event17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014 - Gold Coast, Australia
Duration: 2014 Dec 12014 Dec 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8861
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014
CountryAustralia
CityGold Coast
Period14/12/114/12/5

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sugiyama, A., & Sugawara, T. (2014). Autonomous strategy determination with learning of environments in multi-agent continuous cleaning. In H. K. Dam, J. Pitt, Y. Xu, G. Governatori, & T. Ito (Eds.), PRIMA 2014: Principles and Practice of Multi-Agent Systems - 17th International Conference, Proceedings (pp. 455-462). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8861). Springer Verlag. https://doi.org/10.1007/978-3-319-13191-7_36