Abstract
We present first results from a comparison between a Fuzzy Classifier System operating at the level of whole rule-bases, and three variants of one that operates at the level of individual rules. The application domain is mobile robotics, and the problem is autonomous acquisition of an “investigative” obstacle avoidance competency. The Fuzzy Classifier Systems operate on the rules of fuzzy controllers with pre-defined fuzzy membership functions. Generally, all of the methods used were capable of producing fuzzy controllers with competencies that exceeded that of a simple hand-coded fuzzy controller that we had devised. The approach operating at the level of whole rule-bases yielded more robust and stable convergence on high performance solutions than any other architecture presented here. It is clear from the results that more work needs to be done to unravel the disappointing convergence dynamics of the algorithms operating at the level of individual rules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Booker L, Goldberg D & Holland J (1989). Classifier Systems & GAs, AI 40, pp.235–282.
Smith S F (1980). A learning system based on genetic adaptive algorithms. PhD thesis, Univ. Pittsburgh.
Carse B, Fogarty T C & Munro A (1996). Evolving Fuzzy Rule-based Controllers using Genetic Algorithms. Fuzzy Sets and Systems 80(3), pp.273–293.
Bonarini A (1999). Fuzzy and Crisp Representations of Real-valued Input for Learning Classifier Systems. Procs. GECCO 99, Morgan-Kaufmann, pp.52–59.
Valenzuela-Rendon M (1991). The Fuzzy Classifier System: Motivations and first results. Parallel Problem Solving from Nature (PPSNII), Springer-Verlag, pp.330–334.
Valenzuela-Rendon M (1991). The Fuzzy Classifier System: a Classifier System for Continuously Varying Variables. Procs. 4th Int. Conf. on Genetic Algorithms, pp.346–353.
Wilson S W (1987). Classifier Systems & the Animat Problem. Machine Learning 2 (3), pp.199–228.
Bonarini A (1993). ELF: Learning incomplete fuzzy rule sets for an autonomous robot. Procs. 1st European Congress on Intelligent Technologies and Soft Computing (EUFIT’ 93), pp.69–75.
Bonarini A (1997). Anytime learning and adaptation of hierarchical fuzzy logic behaviors. Journal of Adaptive Behavior 5(3–4), pp.281–315.
Bonarini A & Basso F (1997). Learning to coordinate fuzzy behaviors for autonomous agents. Int. Journal of Approximate Reasoning, F. Herrera (Ed.), 17(4), pp.409–432.
Bonarini A, Bonacina C & Matteucci M (2001). An approach to design of reinforcement functions in the real world, agent based applications. IEEE Trans. SMC. In press.
Nardi D, Adorni G, Chella A, Clemente G, Pagello E & Piaggio M (2000). ART—Azzuro Robot Team. Robocup99—Robot Soccer World Cup III, Springer-Verlag.
Geyer-Schulz A (1997). Fuzzy Rule-Based Expert Systems and Genetic Machine Learning. Series: Studies in Fuzziness & Soft Comp., vol. 3. Springer-Verlag, ISBN: 3790809640.
Pedrycz W (1997). Ed: Fuzzy Evolutionary Computation. Kluwer, ISBN: 0792399420.
Bonarini A (2000). An Introduction to Learning Fuzzy Classifier Systems. P. L. Lanzi, W. Stolzmann and S. W. Wilson (Eds.), Learning Classifier Systems-from Foundations to Applications, Lecture Notes in AI, pp.83–104. Springer Verlag Berlin Heidelberg.
Pipe A G & Carse B (2000). Acquisition of Fuzzy Rules for Mobile Robot Control: 1st Results from 2 Evolutionary Computation Approaches. GECCO00, pp.849–856.
Carse B & Pipe A G, (2001). X-FCS: a fuzzy classifier systems using accuracy based fitness—1st results. Procs. Int. Conf. Fuzzy Logic and Technology, EUSFLAT, pp.195–198.
Mamdani E H & Assilian S (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, vol. 7, no. 1, pp.1–13.
Parodi A & Bonelli P (1993). A New approach to fuzzy classifier systems. Procs. of 5th International Conference on Genetic Algorithms, pp.223–230.
Hwang W & Thompson W (1994). Design of Fuzzy Logic Controllers using Genetic Algorithms. Procs. of the 3rd IEEE Int. Conf. on Fuzzy Systems, pp.1383–1388.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pipe, A.G., Carse, B. (2002). First Results from Experiments in Fuzzy Classifier System Architectures for Mobile Robotics. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_56
Download citation
DOI: https://doi.org/10.1007/3-540-45712-7_56
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44139-7
Online ISBN: 978-3-540-45712-1
eBook Packages: Springer Book Archive