Name: Interdisciplinary Approaches to Robot Learning (en Inglés)
SKU: 25306618
Price: 114.53 USD
Availability: InStock
Author: Birk, Andreas ; Demiris, Yiannis
ISBN: 9789810243203

portada Interdisciplinary Approaches to Robot Learning (en Inglés)

Interdisciplinary Approaches to Robot Learning (en Inglés)

Formato

Libro Físico

Editorial

World Scientific Publishing Company

Autor

Birk, Andreas ; Demiris, Yiannis

Año

2000

Idioma

Inglés

N° páginas

220

Encuadernación

Tapa Dura

Dimensiones

22.4x16x1.8 cm

Peso

0.47 kg.

ISBN

9810243200

ISBN13

9789810243203

Categorías

Medicina: cuestiones generales
Robótica
Inteligencia artificial

Interdisciplinary Approaches to Robot Learning (en Inglés)

Birk, Andreas ; Demiris, Yiannis (Autor) · World Scientific Publishing Company · Tapa Dura

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Reseña del libro "Interdisciplinary Approaches to Robot Learning (en Inglés)"

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.