1995 ALECSYSandtheAutonoMouseLearnin

• (Dorigo, 1995) ⇒ Marco Dorigo. (1995). “ALECSYS and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems.” In: Machine Learning Journal, 19(3). ISBN:0885-6125, 1573-0565 doi:10.1023/A:1022649410928

Subject Headings: ALECSYS; Learning Classifier System.

Cited By

• http://scholar.google.com/scholar?q=%221995%22+ALECSYS+and+the+AutonoMouse%3A+Learning+to+Control+a+Real+Robot+by+Distributed+Classifier+Systems
• http://dl.acm.org/citation.cfm?id=207200.207202&preflayout=flat#citedby

Quotes

Author keywords

• Learning Classifier Systems, Reinforcement Learning, Genetic Algorithms, Animat Problem.

Abstract

In this article we investigate the feasibility of using learning classifier systems as a tool for building adaptive control systems for real robots. Their use on real robots imposes efficiency constraints which are addressed by three main tools: parallelism, distributed architecture, and training. Parallelism is useful to speed up computation and to increase the flexibility of the learning system design. Distributed architecture helps in making it possible to decompose the overall task into a set of simpler learning tasks. Finally, training provides guidance to the system while learning, shortening the number of cycles required to learn. These tools and the issues they raise are first studied in simulation, and then the experience gained with simulations is used to implement the learning system on the real robot. Results have shown that with this approach it is possible to let the AutonoMouse, a small real robot, learn to approach a light source under a number of different noise and lesion conditions.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1995 ALECSYSandtheAutonoMouseLearnin	Marco Dorigo			ALECSYS and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems				10.1023/A:1022649410928		1995