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Research Article
Behaviour Learning System for Robot Soccer Using Neural Network
Moeko Tominaga
Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering: Kyushu Kogyo Daigaku Daigakuin Seimeitai Kogaku Kenkyuka
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0500-4081
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Technological developments have raised the promise of a human{robot symbiotic society. A soccer game has characteristics similar to those expected in such a society. Soccer is a multiagent game in which the strategy employed depends on each agent's position and actions. This study discusses the results of the development of a learning system that uses a self-organising map to select behaviours depending on the situation. This system can reproduce the action selection algorithm of all players in a certain team, and the robot can instantly select the next cooperative action from the information obtained during the game. In this manner, common sense rules can be shared to learn an action selection algorithm for a set of both human and robot agents as opposed to robots alone.
Keywords
Autonomous behaviour selection, Neural network, Human-robot coordination
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CURRENT STATUS: Under Review
Version 1
Posted 21 Jan, 2021
No community comments so far
Review #1 received
Received 20 Feb, 2021
Reviewer #1 agreed
On 25 Jan, 2021
Editor assigned
On 18 Jan, 2021
Reviewers invited
Invitations sent on 18 Jan, 2021
Submission checks complete
On 18 Jan, 2021
Editor invited
On 18 Jan, 2021
First submitted
On 17 Jan, 2021
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Subject Areas
Mechanical Engineering
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This preprint is under consideration at ROBOMECH Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Behaviour Learning System for Robot Soccer Using Neural Network
Moeko Tominaga
Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering: Kyushu Kogyo Daigaku Daigakuin Seimeitai Kogaku Kenkyuka
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0500-4081
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 21 Jan, 2021
No community comments so far
Review #1 received
Received 20 Feb, 2021
Reviewer #1 agreed
On 25 Jan, 2021
Editor assigned
On 18 Jan, 2021
Reviewers invited
Invitations sent on 18 Jan, 2021
Submission checks complete
On 18 Jan, 2021
Editor invited
On 18 Jan, 2021
First submitted
On 17 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Technological developments have raised the promise of a human{robot symbiotic society. A soccer game has characteristics similar to those expected in such a society. Soccer is a multiagent game in which the strategy employed depends on each agent's position and actions. This study discusses the results of the development of a learning system that uses a self-organising map to select behaviours depending on the situation. This system can reproduce the action selection algorithm of all players in a certain team, and the robot can instantly select the next cooperative action from the information obtained during the game. In this manner, common sense rules can be shared to learn an action selection algorithm for a set of both human and robot agents as opposed to robots alone.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.