Multi-agent task allocation based on the learning of managers and local preference selections

Yuka Ishihara, Toshiharu Sugawara

研究成果: Conference article査読

抄録

This paper discusses an adaptive distributed allocation method in which agents individually learn strategies for preferences to decide on the rank of tasks which they want to be allocated by a manager. In a distributed edge-computing environment, multiple managers that control the provision of a variety of services requested from different locations have to allocate the corresponding tasks to appropriate agents, which are usually programs developed by different companies. In our proposed method, each agent learns which manager will allocate tasks it performs well and how to declare its preferred tasks. We experimentally evaluated the proposed learning method and showed that agents using the proposed method could effectively execute requested tasks and could adapt to changes in patterns of the requested tasks.

本文言語English
ページ(範囲)675-684
ページ数10
ジャーナルProcedia Computer Science
176
DOI
出版ステータスPublished - 2020
イベント24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020 - Virtual Online
継続期間: 2020 9 162020 9 18

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)

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