Title :
Evolutionary learning function approximator as robot controller
Author :
Li, Jian Qi ; Chen, Huo Wang ; Wang, Bing Shan
Author_Institution :
Dept. of Comput. Sci., Changsha Inst. of Technol., Hunan, China
Abstract :
In many cases, autonomous robot are required to make decisions repeatedly to select the best action plan among a set of alternatives just according to their indexes of gain and cost. A general robot controller model for such task is outlined. It is found that multi-dimensional monotone functions are sufficient for the comprehensive evaluation of plans. A new Evolutionary Decision Making (EDM) approach, based on evolutionary function approximation by Genetic Algorithms, is proposed to learn the core of such decision making strategy. An application instance on virtual exploring robot controller design is given, which validate the effectiveness of the proposed approach.
Keywords :
control system synthesis; decision making; evolutionary computation; learning (artificial intelligence); robots; action plan; autonomous robot; evolutionary decision making; evolutionary function approximation; general robot controller; genetic algorithms; intelligent decision making; learning robot controller; robot controller design; Automatic control; Computer science; Costs; Decision making; Function approximation; Genetic algorithms; Robot control; Robot sensing systems; Robotics and automation; Testing;
Conference_Titel :
Automation Congress, 2002 Proceedings of the 5th Biannual World
Print_ISBN :
1-889335-18-5
DOI :
10.1109/WAC.2002.1049492
