Multi-Behaviour Robot Control using Genetic Network Programming with Fuzzy Reinforcement Learning-Reference-Cited by-同舟云学术

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Multi-Behaviour Robot Control using Genetic Network Programming with Fuzzy Reinforcement Learning

Published:2015 Issue: Volume: Page:151-158
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Wang W.,Reyes N. H.,Barczak A. L. C.,Susnjak T.,Sincak Peter

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-16841-8_15

Reference8 articles.

1. Sendari, S., Mabu, S., Hirasawa, K.: Fuzzy genetic network programming with reinforcement learning for mobile robot navigation. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, Alaska, USA, pp. 2243–2248. IEEE (2011)

2. Reyes, N.H., Barczak, A.L., Susnjak, T., Sincák, P., Vašcák, J.: Real-Time Fuzzy Logic-based Hybrid Robot Path-Planning Strategies for a Dynamic Environment. In: Efficiency and Scalability Methods for Computational Intellect, pp. 115–141. IGI Global (2013)

3. ISRL;J. Vascák,2014

4. Reyes, N.H., Dadios, E.P.: Dynamic color object recognition using fuzzy logic. JACIII 8(1), 29–38 (2004)

5. Gerdelan, A., Reyes, N.: Synthesizing adaptive navigational robot behaviours using a hybrid fuzzy a* approach. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications, vol. 38, pp. 699–710. Springer, Heidelberg (2006)

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