Download Learning Robots: 6th European Workshop, EWLR-6 Brighton, by Tony Belpaeme, Joris Van Looveren, Luc Steels (auth.), PDF

By Tony Belpaeme, Joris Van Looveren, Luc Steels (auth.), Andreas Birk, John Demiris (eds.)

Robot studying is a wide and interdisciplinary sector. This holds with reference to the elemental pursuits and the scienti c heritage of the researchers concerned, in addition to with reference to the innovations and techniques used. The pursuits that encourage the researchers during this eld diversity from basic examine concerns, corresponding to find out how to constructively comprehend intelligence, to basically software o- ented paintings, resembling the exploitation of studying thoughts for commercial robotics. Given this extensive scope of pursuits, it's not wonderful that, even supposing AI and robotics tend to be the middle of the robotic studying eld, disciplines like cog- tive technological know-how, arithmetic, social sciences, neuroscience, biology, and electric engineering have additionally all started to play a job in it. during this approach, its interdisciplinary personality is greater than a trifling style, and ends up in a effective alternate of principles. one of many goals of EWLR-6 used to be to foster this alternate of principles and to f- ther strengthen contacts among the di erent scienti c components eager about studying robots. EWLR is, frequently, a \European Workshop on studying Robots". however, the organizers of EWLR-6 determined to open up the workshop to non-European study in addition, and integrated within the application committee we- recognized non-European researchers. This process proved to achieve success due to the fact there has been a robust participation within the workshop from researchers outdoor - rope, specifically from Japan, which supplied new principles and result in new contacts.

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Extra resources for Learning Robots: 6th European Workshop, EWLR-6 Brighton, England, August 1–2, 1997 Proceedings

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10. H. Murao and S. Kitamura 500 400 100 200 300 Forward Sensor 600 700 1000 (mm) 28 1000 (mm) Le ft Se ns or 0 ht Rig or ens 1000 (mm) S A projection of the representative sensor vectors at trial 500 onto forward, left and right sensor space. A surrounded area corresponds to the right-lower corner and right-upper corner in Fig. 10. The denseness in the area re
ects the importance of corresponding sensor vectors to accomplish the task. Fig. 11. Modular Reinforcement Learning: An Application to a Real Robot Task Zsolt Kalmउar1, Csaba Szepesvउari2, and Andrउas Líorincz3 1 2 Dept.

Reinforcement learning is concerned with the class of learning problems in which an agent operating in an environment learns how to achieve a given task by trial and error. To do so it adjusts its decision process on the basis of a form of feedback commonly called reinforcement signal. This feedback is a scalar which reæects an internal measure of the robot's performance. The robot is rewarded घpunishedङ at each time step according to how close घhow A. Birk and J. ): EWLR-6, LNAI 1545, pp. 46−61, 1998.

6. Z. Kalmउar, C. Szepesvउari, and A. Líorincz. Generalization in an autonomous agent. In Proc. of IEEE WCCI ICNN'94, volume 3, pages 1815í1817, Orlando, Florida, June 1994. IEEE Inc. 7. Z. Kalmउar, C. Szepesvउari, and A. Líorincz. Generalized dynamic concept model as a route to construct adaptive autonomous agents. Neural Network World, 5:353í360, 1995. 8. Z. Kalmउar, C. Szepesvउari, and A. Líorincz. Module based reinforcement learning: Experiments with a real robot. Machine Learning, 31:55í85, 1998.

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