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/).

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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  -