Behaviour Learning System for Robot Soccer Using Neural Network
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.
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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.
Posted 21 Jan, 2021
Received 20 Feb, 2021
On 25 Jan, 2021
On 18 Jan, 2021
Invitations sent on 18 Jan, 2021
On 18 Jan, 2021
On 18 Jan, 2021
On 17 Jan, 2021
Behaviour Learning System for Robot Soccer Using Neural Network
Posted 21 Jan, 2021
Received 20 Feb, 2021
On 25 Jan, 2021
On 18 Jan, 2021
Invitations sent on 18 Jan, 2021
On 18 Jan, 2021
On 18 Jan, 2021
On 17 Jan, 2021
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.