Name: Probabilistic Approaches to Robotic Perception (en Inglés)
Price: 497.39 PEN
Availability: InStock
Author: Ferreira, João Filipe ; Miranda Dias, Jorge
ISBN: 9783319032894

portada Probabilistic Approaches to Robotic Perception (en Inglés)

Formato

Libro Físico

Autor

João Filipe Ferreira

Jorge Miranda Dias

Editorial

Springer

Idioma

Inglés

N° páginas

242

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 1.5 cm

Peso

0.39 kg.

ISBN13

9783319032894

Categorías

Psicología cognitiva
Robótica
Aplicaciones gráficas y multimedia

Probabilistic Approaches to Robotic Perception (en Inglés)

João Filipe Ferreira (Autor) · Jorge Miranda Dias (Autor) · Springer · Tapa Blanda

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S/ 497,39

S/ 994,77

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Reseña del libro "Probabilistic Approaches to Robotic Perception (en Inglés)"

This book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot? How should a robotic system perceive, infer, decide and act efficiently? These are two of the challenging questions robotics community and robotic researchers have been facing.The development of robotic domain by the 1980s spurred the convergence of automation to autonomy, and the field of robotics has consequently converged towards the field of artificial intelligence (AI). Since the end of that decade, the general public's imagination has been stimulated by high expectations on autonomy, where AI and robotics try to solve difficult cognitive problems through algorithms developed from either philosophical and anthropological conjectures or incomplete notions of cognitive reasoning. Many of these developments do not unveil even a few of the processes through which biological organisms solve these same problems with little energy and computing resources. The tangible results of this research tendency were many robotic devices demonstrating good performance, but only under well-defined and constrained environments. The adaptability to different and more complex scenarios was very limited.In this book, the application of Bayesian models and approaches are described in order to develop artificial cognitive systems that carry out complex tasks in real world environments, spurring the design of autonomous, intelligent and adaptive artificial systems, inherently dealing with uncertainty and the "irreducible incompleteness of models".