@inproceedings{24ce3e0c2ef1483086fd549de0c70595,
title = "Autonomous strategy determination with learning of environments in multi-agent continuous cleaning",
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.",
author = "Ayumi Sugiyama and Toshiharu Sugawara",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-13191-7_36",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "455--462",
editor = "Dam, {Hoa Khanh} and Jeremy Pitt and Yang Xu and Guido Governatori and Takayuki Ito",
booktitle = "PRIMA 2014",
note = "17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014 ; Conference date: 01-12-2014 Through 05-12-2014",
}