Evolving RoboCup2D Agents Based on PSO
- DOI
- 10.2991/cmfe-15.2015.197How to use a DOI?
- Keywords
- RoboCup2D; agent; architecture; planning; particle swarm optimization
- Abstract
In order to build intelligent robots to accomplish soccer game tasks, this paper introduces evolutionary computing in agent architecture for perception, planning, and action: (1) an architecture based on PSO is proposed, which made up of 4 levels: atomic action, combo action, behavior and policy. (2) by offline training, agents format perception rules and relevant parameters, to optimize perception method for the position, orientation and other information; (3) according to the granularity, functions, and parameters manually specified, PSO builds a set of combo actions, which described by atomic actions, parameters and execution results; (4) according to game environment and a few task rules, PSO searches for task, behavior, and combo actions, as a whole, to accomplish the game tasks. The simulation experiments on RoboCup2D platform show that, agent based on PSO is a robust and flexible robot control method: given evaluation methods and implementation frames, it is able to learn rapidly in real environment, and displays planning behavior without the use of classical planning techniques.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhao Liu AU - Chunchen Dong AU - Shaoxiong YE AU - Yang Yang PY - 2015/07 DA - 2015/07 TI - Evolving RoboCup2D Agents Based on PSO BT - Proceedings of the International Conference on Chemical, Material and Food Engineering PB - Atlantis Press SP - 835 EP - 838 SN - 2352-5401 UR - https://doi.org/10.2991/cmfe-15.2015.197 DO - 10.2991/cmfe-15.2015.197 ID - Liu2015/07 ER -