Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)'s Advances in Robot Learning: 8th European Workshop on PDF

By Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)

This booklet constitutes the completely refereed post-workshop court cases of the eighth ecu Workshop on studying Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised complete workshop papers awarded have been conscientiously reviewed and chosen for inclusion within the booklet. additionally incorporated are invited complete papers. one of the themes addressed are map development for robotic navigation, multi-task reinforcement studying, neural community ways, example-based studying, located brokers, making plans maps for cellular robots, direction discovering, independent robots, and biologically encouraged approaches.

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Additional resources for Advances in Robot Learning: 8th European Workshop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings

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Owen, 2000. Owen, C. (2000). Map-Building and Map-Interpretation Mechanisms for a Mobile Robot. PhD thesis, Department of Computer Science, University of Manchester. to appear. , 1992. , and Flannery, B. (1992). Numerical Recipes in C. Cambridge University Press, Cambridge. , 1982. , and Erlbaum, C. (1982). A neural model for category learning. Biological Cybernetics, 45:35–41. Learning a Navigation Task in Changing Environments by Multi-task Reinforcement Learning Axel Großmann and Riccardo Poli School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, UK Abstract.

Some uncertainty will remain in the agent’s belief about the current state of the world. The usual approach is to use memory of previous actions and observations to aid the disambiguation of the states in the world. In this work, we assume that a method for state disambiguation is available. Although there may be a great deal of uncertainty about the effects of an agent’s actions, there is never any uncertainty about the agent’s current state. 2 Speeding Up the Learning Process by Incremental Learning Ideally, we would like to build mobile robots that learn incrementally and that adapt to changes in their environment during their entire lifetime.

This means that the representations used by the learning method must support incremental learning. Tree-structured representations [18] and constructive neural networks do so and therefore can solve these two problems. Here, we use connectionist function approximators. Since incremental learning in neural networks using distributed representations is still an open issue, we decided to use neural networks that are based on local representations only. The aim of this work is to make a contribution towards the development of continual learning methods applicable to mobile robots.

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