.png){kind=icon}
.png){kind=icon}
返回
A framework based on evolutionary algorithm for strategy optimization in robot soccer
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-018-3376-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In any competitive and uncertain environment, designing an optimal strategy is a challenging task. The manual hand-coding of strategy is a tedious job, and its evaluation on all possible situations becomes even more complicated. This paper proposes a novel distributed framework, named FEASO, based on evolutionary algorithms, for strategy optimization in the domain of robot soccer. In the context of robot soccer, strategy denotes the critical areas where home team agents should be positioned. The focus of this study is to optimize the strategic placements of agents that are defending the goal. The presented approach comprises three modules: evolutionary algorithm execution, parallel fitness evaluation and fitness computation. It executes matches in parallel on different machines for fitness evaluation. The fitness function takes into account three parameters: the goal difference, regions occupied by defending players and ball possession by the home team players. The framework has been successfully implemented in our 3D soccer simulation team that participates in RoboCup event. Experiments are conducted using binaries of various teams taking part in the competition. A comparison of strategies between teams is conducted and analyzed. The results clearly demonstrate that the team that executes optimized strategy is able to defend more goals as compared to the team with hand-coded strategic points.
Keyword:
Opponent
Strategy optimization
Autonomous robots
Evolutionary algorithms
RoboCup Soccer Simulation 3D
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。