Learning of activity cycle length based on battery limitation in multi-agent continuous cooperative patrol problems

Ayumi Sugiyama, Lingying Wu, Toshiharu Sugawara

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

We propose a learning method that decides the appropriate activity cycle length (ACL) according to environmental characteristics and other agents' behavior in the (multi-agent) continuous cooperative patrol problem. With recent advances in computer and sensor technologies, agents, which are intelligent control programs running on computers and robots, obtain high autonomy so that they can operate in various fields without pre-defined knowledge. However, cooperation/coordination between agents is sophisticated and complicated to implement. We focus on the ACL which is time length from starting patrol to returning to charging base for cooperative patrol when agents like robots have batteries with limited capacity. Long ACL enable agent to visit distant location, but it requires long rest. The basic idea of our method is that if agents have long-life batteries, they can appropriately shorten the ACL by frequently recharging. Appropriate ACL depends on many elements such as environmental size, the number of agents, and workload in an environment. Therefore, we propose a method in which agents autonomously learn the appropriate ACL on the basis of the number of events detected per cycle. We experimentally indicate that our agents are able to learn appropriate ACL depending on established spatial divisional cooperation.

元の言語English
ホスト出版物のタイトルICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence
編集者Ana Rocha, Luc Steels, Jaap van den Herik
出版者SciTePress
ページ62-71
ページ数10
ISBN(電子版)9789897583506
出版物ステータスPublished - 2019 1 1
イベント11th International Conference on Agents and Artificial Intelligence, ICAART 2019 - Prague, Czech Republic
継続期間: 2019 2 192019 2 21

出版物シリーズ

名前ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence
1

Conference

Conference11th International Conference on Agents and Artificial Intelligence, ICAART 2019
Czech Republic
Prague
期間19/2/1919/2/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

フィンガープリント Learning of activity cycle length based on battery limitation in multi-agent continuous cooperative patrol problems' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Sugiyama, A., Wu, L., & Sugawara, T. (2019). Learning of activity cycle length based on battery limitation in multi-agent continuous cooperative patrol problems. : A. Rocha, L. Steels, & J. van den Herik (版), ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence (pp. 62-71). (ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence; 巻数 1). SciTePress.