Evaluation of genetic-fuzzy systems in the configuration space

Author

Bonarini, Andrea ; Fiorellato, Fabio

Author_Institution

Dipt. di Elettronica e Inf., Politecnico di Milano, Italy

Volume

2

fYear

2001

fDate

25-28 July 2001

Firstpage

1235

Abstract

We propose an approach to ground the design of learning systems on the analysis of the configuration space of the learning device (e.g., a robot) and on the interpretation of input data. We focus on learning fuzzy classifier systems adopted to evolve behavioral controllers for autonomous robots. We show how it is possible to define some indexes to evaluate objectively both the learning process and the evolved system, thus supporting their designing with engineering principles

Keywords

fuzzy logic; fuzzy systems; genetic algorithms; learning (artificial intelligence); learning systems; mobile robots; autonomous robots; behavioral controllers; configuration space; evolutionary robotics; fuzzy rule bases; genetic algorithms; genetic-fuzzy systems; input data interpretation; learning fuzzy classifier systems; learning systems; mobile robots; reinforcement learning; Artificial intelligence; Autonomous agents; Design engineering; Fuzzy systems; Genetics; Ground support; Learning systems; Orbital robotics; Robot control; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.944783

Filename

944783