Authors
Moeko Tominaga1, *, Yasunori Takemura2, Kazuo Ishii1
1Graduate School of Life and Science Systems Engineering, Kyushu Institute
of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0196,
Japan
2Department of Engineering, Nishinippon Institute of Technology Technology,
1-11 Aratsu, Kanda-town, Miyako-gun, Fukuoka 800-0397, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Moeko Tominaga
Received 29 November 2019, Accepted 1 April 2020, Available Online 2 June
2020.
DOI
https://doi.org/10.2991/jrnal.k.200528.002
Keywords
Strategy; self-organizing map; team behavior; Tensor SOM; multi-agent system;
human–robot cooperation
Abstract
With the progress of technology, the realization of a symbiotic society
with human beings and robots sharing the same environment has become an
important subject. An example of this kind of systems is soccer game. Soccer
is a multi-agent game that requires strategies by taking into account each
member’s position and actions. In this paper, we discuss the results of
the development of a learning system that uses self-organizing map to select
behaviors depending on the situation. A set of possible actions in soccer
game is decided in advance and the algorithm is able to select the best
option, given some specific conditions.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license
(http://creativecommons.org/licenses/by-nc/4.0/).