John demiris (12 Ergebnisse)

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Zustand: New. In English.

Zustand: Very Good. 2000th Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.…

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Hardcover. Zustand: Brand New. 208 pages. 8.75x6.50x0.50 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Robot learning is a broad and interdisciplinary area. This holds with regard to the basic interests and the scientific background of the researchers involved, as well as with regard to the techniques and approaches used. The interests that motivate the researchers in this field range from fundamental research issues, such as how to constructively understand intelligence, to purely application oriented work, such as the exploitation of learning techniques for industrial robotics. Given this broad scope of interests, it is not surprising that, although AI and robotics are usually the core of the robot learning field, disciplines like cognitive science, mathematics, social sciences, neuroscience, biology, and electrical engineering have also begun to play a role in it. In this way, its interdisciplinary character is more than a mere fashion, and leads to a productive exchange of ideas.One of the aims of EWLR-6 was to foster this exchange of ideas and to further boost contacts between the different scientific areas involved in learning robots. EWLR is, traditionally, a European Workshop on Learning Robots. Nevertheless, the organizers of EWLR-6 decided to open up the workshop to non-European research as well, and included in the program committee well-known non-European researchers. This strategy proved to be successful since there was a strong participation in the workshop from researchers outside Europe, especially from Japan, which provided new ideas and lead to new contacts. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Robot learning is an exciting and interdisciplinary eld. This state is re ected in the range and form of the papers presented here. Techniques that have - come well established in robot learning are present: evolutionary methods, neural networkapproaches, reinforcement learning; as are techniques from control t- ory, logic programming, and Bayesian statistics. It is notalbe that in many of the papers presented in this volume several of these techniques are employed in conjunction. In papers by Nehmzow, Grossmann and Quoy neural networks are utilised to provide landmark-based representations of the environment, but di erent techniques are used in each paper to make inferences based on these representations. Biology continues to provide inspiration for the robot learning researcher. In their paper Peter Eggenberger et al. borrow ideas about the role of n- romodulators in switching neural circuits, These are combined with standard techniques from arti cial neural networks and evolutionary computing to p- vide a powerful new algorithm for evolving robot controllers. In the nal paper in this volume Bianco and Cassinis combine observations about the navigation behaviour of insects with techniques from control theory to produce their visual landmarklearning system. Hopefully this convergence of engineering and biol- ical approaches will continue. A rigourous understanding of the ways techniques from these very di erent disciplines can be fused is an important challenge if progress is to continue. Al these papers are also testament to the utility of using robots to study intelligence and adaptive behaviour.…

Zustand: New. This collection of papers explore the variety of techniques used to improve robot learning. The contributions are interdisciplinary in nature and combine research from the fields of robotics, computer science and biology.KlappentextrnrnRobot.

Buch. Zustand: Neu. Neuware - Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangerous for humans. Robots can also inhabit dynamic environments, for example to operate among humans, not just in factories, but also taking on more active roles. Recently, for instance, they have made their way into the home entertainment market. Given the variety of situations that robots will be placed in, learning becomes increasingly important.Robot learning is essentially about equipping robots with the capacity to improve their behaviour over time, based on their incoming experiences. The papers in this volume present a variety of techniques. Each paper provides a mini-introduction to a subfield of robot learning. Some also give a fine introduction to the field of robot learning as a whole. There is one unifying aspect to the work reported in the book, namely its interdisciplinary nature, especially in the combination of robotics, computer science and biology. This approach has two important benefits: first, the study of learning in biological systems can provide robot learning scientists and engineers with valuable insights into learning mechanisms of proven functionality and versatility; second, computational models of learning in biological systems, and their implementation in simulated agents and robots, can provide researchers of biological systems with a powerful platform for the development and testing of learning theories.…