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

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

This ebook constitutes the completely refereed post-workshop complaints of the eighth eu Workshop on studying Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised complete workshop papers offered have been rigorously reviewed and chosen for inclusion within the e-book. additionally incorporated are invited complete papers. one of the subject matters addressed are map development for robotic navigation, multi-task reinforcement studying, neural community techniques, example-based studying, positioned brokers, making plans maps for cellular robots, direction discovering, self reliant robots, and biologically encouraged approaches.

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

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Interestingly, neuroscientific results suggest that biological networks not only adjust the synaptic weights, but also the neural structure by blocking or activating synapses or neurons by the use of signaling molecules, so called neuromodulators [Meyrand91]. These findings stem from investigations made with the lobster’s stomatogastric nervous system in which certain active neurons diffuse 46 P. Eggenberger et al. neuromodulators which then rearrange the networks. Note that the effect of a neuromodulator depends not only on theses substances, but also on the specific receptors, which are differently expressed in different cells.

It also specifies a criterion for splitting (and possibly merging) states. Recently, McCallum [18] defined such a criterion. He suggested to distinguish states that have different policy actions or different utilities, and merge states that have the same policy action and same utility. This utile distinction test yields a task-dependent state representation. 3 Utility-Based State Distinction Often a perceptually aliased state that affects utility will have wildly fluctuating reward values. However, we cannot base a state splitting test solely on reward variance because some changes in reward are caused by the stochastic nature of the world.

This model is able to generate dimensionality reducing mappings. These mappings are able to preserve neighbourhood relations in the input data and have the property to represent regions of high input density on correspondingly large parts of the topological structure. In contrast to Kohonen’s self-organising feature map [12], which serves similar purposes, neither the number of units nor the exact topology has to be predefined in this model. Instead, a growth process successively inserts units and connections.

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