Strategies for Energy-Aware Multi-agent Continuous Cooperative Patrolling Problems Subject to Requirements

Lingying Wu*, Toshiharu Sugawara

*この研究の対応する著者

研究成果: Conference contribution

抄録

This paper proposes a method of autonomous strategy learning for multiple cooperative agents integrated with a series of behavioral strategies aiming at reduction of energy cost on the premise of satisfying quality requirements in continuous patrolling problems. We improved our algorithm of requirement estimation to avoid concentration of agents since they are given the knowledge of the work environment in advance. The experimental results show that our proposal enables the agents to learn to select appropriate behavioral planning strategies according to performance efficiency and energy cost, and to individually estimate whether the given requirement is reached and modify their action plans to save energy. Furthermore, agents with the new requirement estimation method could achieve fair patrolling by introducing local observations.

本文言語English
ホスト出版物のタイトルPRIMA 2019
ホスト出版物のサブタイトルPrinciples and Practice of Multi-Agent Systems - 22nd International Conference, Proceedings
編集者Matteo Baldoni, Mehdi Dastani, Beishui Liao, Yuko Sakurai, Rym Zalila Wenkstern
出版社Springer
ページ585-593
ページ数9
ISBN(印刷版)9783030337919
DOI
出版ステータスPublished - 2019
イベント22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019 - Turin, Italy
継続期間: 2019 10 282019 10 31

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11873 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019
国/地域Italy
CityTurin
Period19/10/2819/10/31

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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