Abstract
In this paper we utilize information theory to study the impact in learning performance of various motivation and environmental configurations. This study is done within the context of an evolutionary fuzzy motivation based approach used for acquiring behaviors in mobile robot exploration of complex environments. Our robot makes use of a neural network to evaluate measurements from its sensors in order to establish its next behavior. Adaptive learning, fuzzy based fitness and Action-based Environment Modeling (AEM) are integrated and applied toward training the robot. Using information theory we determine the conditions that lead the robot toward highly fit behaviors. The research performed also shows that information theory is a useful tool in analyzing robotic training methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge (1998)
Park, H., Kim, E., Kim, H.: Robot Competition Using Gesture Based Interface. In: Hromkovič, J., Nagl, M., Westfechtel, B. (eds.) WG 2004. LNCS, vol. 3353, pp. 131–133. Springer, Heidelberg (2004)
Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A., Siegwart, R.: Robots Meet Humans - Interacion in Public Spaces. IEEE Transactions on Industrial Electronics 52(6), 1530–1546 (2006)
Arredondo, T., Freund, W., Muñoz, C., Navarro, N., Quirós, F.: Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 462–471. Springer, Heidelberg (2006)
Huitt, W.: Motivation to learn: An overview. Educational Psychology Interactive (2001), http://chiron.valdosta.edu/whuitt/col/motivation/motivate.html
Tan, K.C., Goh, C.K, Yang, Y.J., Lee, T.H.: Evolving better population distribution and exploration in evolutionary multi-objective optimization. European Journal of Operations Research 171, 463–495 (2006)
Chalmers, D.J.: The evolution of learning: An experiment in genetic connectionism. In: Proceedings of the 1990 Connectionist Models Summer School, pp. 81–90. M. Kaufmann, San Mateo, CA (1990)
YAKS simulator website: http://www.his.se/iki/yaks
Yamada, S.: Recognizing environments from action sequences using self-organizing maps. Applied Soft Computing 4, 35–47 (2004)
Teuvo, K.: The self-organizing map. Proceedings of the IEEE 79(9), 1464–1480 (1990)
Cover, T., Thomas, J.: Elements of Information Theory. Wiley, New York (1991)
Handmann, U., Kalinke, T., Tzomakas, C., Werner, M., Weelen, W.v.: An image processing system for driver assistance. Image and Vision Computing 18, 367–376 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arredondo V., T., Freund, W., Muñoz, C., Quirós, F. (2007). Learning Performance in Evolutionary Behavior Based Mobile Robot Navigation. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_77
Download citation
DOI: https://doi.org/10.1007/978-3-540-76631-5_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
Online ISBN: 978-3-540-76631-5
eBook Packages: Computer ScienceComputer Science (R0)