|
Showing 1 - 3 of
3 matches in All Departments
Building a robot that learns to perform a task has been
acknowledged as one of the major challenges facing artificial
intelligence. Self-improving robots would relieve humans from much
of the drudgery of programming and would potentially allow
operation in environments that were changeable or only partially
known. Progress towards this goal would also make fundamental
contributions to artificial intelligence by furthering our
understanding of how to successfully integrate disparate abilities
such as perception, planning, learning and action. Although its
roots can be traced back to the late fifties, the area of robot
learning has lately seen a resurgence of interest. The flurry of
interest in robot learning has partly been fueled by exciting new
work in the areas of reinforcement earning, behavior-based
architectures, genetic algorithms, neural networks and the study of
artificial life. Robot Learning gives an overview of some of the
current research projects in robot learning being carried out at
leading universities and research laboratories in the United
States. The main research directions in robot learning covered in
this book include: reinforcement learning, behavior-based
architectures, neural networks, map learning, action models,
navigation and guided exploration.
Representations are at the heart of artificial intelligence (AI).
This book is devoted to the problem of representation discovery:
how can an intelligent system construct representations from its
experience? Representation discovery re-parameterizes the state
space - prior to the application of information retrieval, machine
learning, or optimization techniques - facilitating later inference
processes by constructing new task-specific bases adapted to the
state space geometry. This book presents a general approach to
representation discovery using the framework of harmonic analysis,
in particular Fourier and wavelet analysis. Biometric compression
methods, the compact disc, the computerized axial tomography (CAT)
scanner in medicine, JPEG compression, and spectral analysis of
time-series data are among the many applications of classical
Fourier and wavelet analysis. A central goal of this book is to
show that these analytical tools can be generalized from their
usual setting in (infinite-dimensional) Euclidean spaces to
discrete (finite-dimensional) spaces typically studied in many
subfields of AI. Generalizing harmonic analysis to discrete spaces
poses many challenges: a discrete representation of the space must
be adaptively acquired; basis functions are not pre-defined, but
rather must be constructed. Algorithms for efficiently computing
and representing bases require dealing with the curse of
dimensionality. However, the benefits can outweigh the costs, since
the extracted basis functions outperform parametric bases as they
often reflect the irregular shape of a particular state space. Case
studies from computer graphics, information retrieval, machine
learning, and state space planning are used to illustrate the
benefits of the proposed framework, and the challenges that remain
to be addressed. Representation discovery is an actively developing
field, and the author hopes this book will encourage other
researchers to explore this exciting area of research. Table of
Contents: Overview / Vector Spaces / Fourier Bases on Graphs /
Multiscale Bases on Graphs / Scaling to Large Spaces / Case Study:
State-Space Planning / Case Study: Computer Graphics / Case Study:
Natural Language / Future Directions
Building a robot that learns to perform a task has been
acknowledged as one of the major challenges facing artificial
intelligence. Self-improving robots would relieve humans from much
of the drudgery of programming and would potentially allow
operation in environments that were changeable or only partially
known. Progress towards this goal would also make fundamental
contributions to artificial intelligence by furthering our
understanding of how to successfully integrate disparate abilities
such as perception, planning, learning and action. Although its
roots can be traced back to the late fifties, the area of robot
learning has lately seen a resurgence of interest. The flurry of
interest in robot learning has partly been fueled by exciting new
work in the areas of reinforcement earning, behavior-based
architectures, genetic algorithms, neural networks and the study of
artificial life. Robot Learning gives an overview of some of the
current research projects in robot learning being carried out at
leading universities and research laboratories in the United
States. The main research directions in robot learning covered in
this book include: reinforcement learning, behavior-based
architectures, neural networks, map learning, action models,
navigation and guided exploration.
|
You may like...
Barbie
Margot Robbie, Ryan Gosling
Blu-ray disc
R266
Discovery Miles 2 660
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
The Flash
Ezra Miller, Michael Keaton, …
DVD
R264
Discovery Miles 2 640
|