October 1998
New Books
Spatial Optimization for Managed Ecosystems
"Spatial Optimization for Managed Ecosystems" presents ideas and methods for directly optimizing the spatial layout of management actions that influence the landscape features in which an ecosystem functions.
The first of four parts treats static spatial relationships that reflect the importance of shape, size and proximity within an ecosystem. Part two considers spatial autocorrelation in a chance-constrained modeling framework. Part three discusses dynamic spatial changes within modeled ecosystems, and the final section focuses on diversity and sustainability.
Most discussion concerns wildlife habitat issues, but authors John Hof and Michael Bevers also include chapters on recreation, timber management, water runoff and pest management.
"Spatial Optimization for Managed Ecosystems" is the ideal introduction to spatial optimization theory and technique for landscape ecologists, forest managers, conservation biologists, and students of ecology and resource management.
Published by Columbia University Press.
on the Reader Service Form
Graphical Models for Machine Learning and Digital Communication
A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In "Graphical Models for Machine Learning and Digital Communication," author Brendan J. Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression and channel coding.
Using probabilistic structures such as Bayesian belief networks and Markov random fields, he describes the relationships between random variables in these systems and how to apply graph-based inference techniques to develop new algorithms.
Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterative turbodecoding algorithm (currently one of the best error-correcting decoding algorithm), the bits-back coding method, the Markov chain Monte Carlo technique and variational inference.
Published by The MIT Press.
on the Reader Service Form
Reinforcement Learning — An Introduction
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment.
In "Reinforcement Learning," authors Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
The book is divided into three parts: Part one defines the reinforcement learning problem in terms of Markov decision problems; Part two provides basic solution methods (i.e., dynamic programming, Monte Carlo simulation and temporal-difference learning); Part three presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces and planning. The two final chapters present case studies and consider the future of reinforcement learning.
Published by The MIT Press
on the Reader Service Form
Introduction to Partial Differential Equations
"Introduction to Partial Differential Equations" teaches the basic methods of partial differential equations and introduces related important ideas associated with the analysis of numerical methods for those partial differential equations. Standard topics such as separation of variables, Fourier analysis, maximum principles and energy estimates are included.
Numerical methods are introduced in parallel to the classical theory. The numerical experiments are used to illustrate properties of differential equations, and a theory for finite difference approximations is developed.
Authors Aslak Tveito and Ragnar Winther present the material at an easy pace with well-organized exercises ranging from the straightforward to the challenging. Additionally, special projects are including containing step-by-step hints and instructions to help guide students in the correct way of approaching partial differential equations.
The text would be suitable for advanced undergraduate and graduate courses in mathematics and engineering.
Published by Springer-Verlag.
on the Reader Service Form
The Organization of Integrated Product Development
In today's industries, New product development (NPD) is often the focal point of competition. Companies that are able to effectively develop, produce and introduce new products are the key competitors in markets where variety and time-to-market play an increasingly important role.
"The Organization of Integrated Product Development" aims to answer the question: Which integration mechanisms lead to effective coordination and overlap of NPD activities in which situations? Author Victor Paashuis examines measures aimed at improving the cooperation and communication between marketing, engineering and production departments. The mechanisms, strategies and goals, knowledge and skills, and organizational arrangements are presented and their impact on the results of NPD projects and relationships are also discussed.
Providing an in-depth understanding of the background and theory, as well as using detailed case studies to illustrate both the human and organization issues in practice, this book will be of interest to managers in industry, particularly those in product development, and to academics and students of technology and management.
Published by Springer-Verlag.
on the Reader Service Form
OR/MS Today copyright © 1998 by the Institute for Operations Research and the Management Sciences. All rights reserved.
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