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Analysis and Design of Robot’s Behavior: Towards a Methodology

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Book cover Learning Robots (EWLR 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1545))

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Abstract

We introduce a methodology to design reinforcement based control architectures for autonomous robots. It aims at systematizing the behavior analysis and the controller design. The methodology has to be seen as a conceptual framework in which a number of methods are to be defined. In this paper we use some more or less known methods to show the feasibility of the methodology. The postman-robot case study illustrates how the proposed methodology is applied.

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References

  1. Valentino Braitenberg. Vehicles. Experiments In Synthetic Psychology. MIT Press, 1984.

    Google Scholar 

  2. Marco Colombetti, Marco Dorigo, and Giuseppe Borghi. Behavior analysis and design-a methodology for behavior engineering. IEEE Transactions on Systems, Man and Cybernetics, 26, 1996.

    Google Scholar 

  3. Peter Dayan and Geoffrey E. Hinton. Feudal reinforcement learning. In Advances in Neural Information Processing Systems 5, 1993.

    Google Scholar 

  4. Thomas G. Dietterich. Hierarchical reinforcement learning with the MAXQ value function decomposition. Technical report, Oregon State University, 1997.

    Google Scholar 

  5. Leslie P. Kaelbling, Michael L. Littman, and Andrew W. Moore. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4, 1996.

    Google Scholar 

  6. Long J. Lin. Reinforcement learning with hidden state. In Proceedings of the Second International Conference on Simulation of Adaptive Behavior, 1992.

    Google Scholar 

  7. Long J. Lin. Hierarchical learning of robot skills by reinforcement. In Proceedings of the IEEE International Conference on Neural Networks, 1993.

    Google Scholar 

  8. Maja J. Mataric. Reward functions for accelerated learning. In W.W. Cohen and H. Hirsh, editors, Proceedings of the Eleventh International Conference on Machine Learning. Morgan Kaufmann, 1994.

    Google Scholar 

  9. Jose del R. Millan. Rapid, safe and incremental learning of navigation strategies. IEEE Transactions on Systems, Man and Cybernetics, 26, 1996.

    Google Scholar 

  10. Michel Minoux. Mathematical Programming. John Wiley and Son, 1986.

    Google Scholar 

  11. Doina Precup, Richard S. Sutton, and Satinder P. Singh. Planning with closed-loop macro actions. Working notes of the 1997 AAAI Fall Symposium on Model-directed Autonomous Systems, 1997.

    Google Scholar 

  12. Gavin A. Rummery. Problem Solving With Reinforcement Learning. PhD thesis, University of Cambridge, 1995.

    Google Scholar 

  13. Richard S. Sutton. Implementation details of the TD(⋋) procedure for the case of vector predictions and backpropagation. Technical Report TN87-509.1, GTE Laboratories, 1989.

    Google Scholar 

  14. Sebastian Thrun. Efficient exploration in reinforcement learning. Technical Report CMU-CS-92-102, Carnegie Mellon University, 1992.

    Google Scholar 

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© 1998 Springer-Verlag Berlin Heidelberg

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Faihe, Y., Müller, JP. (1998). Analysis and Design of Robot’s Behavior: Towards a Methodology. In: Birk, A., Demiris, J. (eds) Learning Robots. EWLR 1997. Lecture Notes in Computer Science(), vol 1545. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49240-2_4

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  • DOI: https://doi.org/10.1007/3-540-49240-2_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65480-3

  • Online ISBN: 978-3-540-49240-5

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