Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization
The 2D soccer simulation league is one of the best test beds for the research of artificial intelligence (AI). It has achieved great successes in the domain of multi-agent cooperation and machine learning. However, the problem of integral offensive strategy has not been solved because of the dynamic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2016-02-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.5772/62167 |
| _version_ | 1828822951569391616 |
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| author | Shengbing Chen Gang Lv Xiaofeng Wang |
| author_facet | Shengbing Chen Gang Lv Xiaofeng Wang |
| author_sort | Shengbing Chen |
| collection | DOAJ |
| description | The 2D soccer simulation league is one of the best test beds for the research of artificial intelligence (AI). It has achieved great successes in the domain of multi-agent cooperation and machine learning. However, the problem of integral offensive strategy has not been solved because of the dynamic and unpredictable nature of the environment. In this paper, we present a novel offensive strategy based on multi-group ant colony optimization (MACO-OS). The strategy uses the pheromone evaporation mechanism to count the preference value of each attack action in different environments, and saves the values of success rate and preference in an attack information tree in the background. The decision module of the attacker then selects the best attack action according to the preference value. The MACO-OS approach has been successfully implemented in our 2D soccer simulation team in RoboCup competitions. The experimental results have indicated that the agents developed with this strategy, along with related techniques, delivered outstanding performances. |
| first_indexed | 2024-12-12T13:23:56Z |
| format | Article |
| id | doaj.art-0aa298724039438da427726747c75858 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-12T13:23:56Z |
| publishDate | 2016-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-0aa298724039438da427726747c758582022-12-22T00:23:12ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142016-02-011310.5772/6216710.5772_62167Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony OptimizationShengbing Chen0Gang Lv1Xiaofeng Wang2 Key Lab of Network and Intelligent Information Processing, Department of Computer Science and Technology, Hefei University, Hefei, China Key Lab of Network and Intelligent Information Processing, Department of Computer Science and Technology, Hefei University, Hefei, China Key Lab of Network and Intelligent Information Processing, Department of Computer Science and Technology, Hefei University, Hefei, ChinaThe 2D soccer simulation league is one of the best test beds for the research of artificial intelligence (AI). It has achieved great successes in the domain of multi-agent cooperation and machine learning. However, the problem of integral offensive strategy has not been solved because of the dynamic and unpredictable nature of the environment. In this paper, we present a novel offensive strategy based on multi-group ant colony optimization (MACO-OS). The strategy uses the pheromone evaporation mechanism to count the preference value of each attack action in different environments, and saves the values of success rate and preference in an attack information tree in the background. The decision module of the attacker then selects the best attack action according to the preference value. The MACO-OS approach has been successfully implemented in our 2D soccer simulation team in RoboCup competitions. The experimental results have indicated that the agents developed with this strategy, along with related techniques, delivered outstanding performances.https://doi.org/10.5772/62167 |
| spellingShingle | Shengbing Chen Gang Lv Xiaofeng Wang Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization International Journal of Advanced Robotic Systems |
| title | Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization |
| title_full | Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization |
| title_fullStr | Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization |
| title_full_unstemmed | Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization |
| title_short | Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization |
| title_sort | offensive strategy in the 2d soccer simulation league using multi group ant colony optimization |
| url | https://doi.org/10.5772/62167 |
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