Multi-Agent Task Allocation Based on the Learning of Managers and Local Preference Selections
| Autor: | Yuka Ishihara, Toshiharu Sugawara |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science Multi-agent system Rank (computer programming) Control (management) 02 engineering and technology Preference Variety (cybernetics) Task (computing) 020901 industrial engineering & automation Human–computer interaction 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Reinforcement learning 020201 artificial intelligence & image processing Edge computing General Environmental Science |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|