State-Space Search: Algorithms, Complexity, Extensions, and Applications - Hardcover

Zhang, Weixiong

Hardcover

ISBN 10: 0387988327 ISBN 13: 9780387988320

Publisher: Springer, 1999

Synopsis

This book is about problem solving. Specifically, it is about heuristic state-space search under branch-and-bound framework for solving com­ binatorial optimization problems. The two central themes of this book are the average-case complexity of heuristic state-space search algorithms based on branch-and-bound, and their applications to developing new problem-solving methods and algorithms. Heuristic state-space search is one of the fundamental problem-solving techniques in Computer Science and Operations Research, and usually constitutes an important component of most intelligent problem-solving systems. The search algorithms considered in this book can be classified into the category of branch-and-bound. Branch-and-bound is a general problem-solving paradigm, and is one of the best techniques for optimally solving computation-intensive problems, such as scheduling and planning. The main search algorithms considered include best-first search, depth­ first branch-and-bound, iterative deepening, recursive best-first search, and space-bounded best-first search. Best-first search and depth-first branch-and-bound are very well known and have been used extensively in Computer Science and Operations Research. One important feature of depth-first branch-and-bound is that it only requires space this is linear in the maximal search depth, making it very often a favorable search algo­ rithm over best-first search in practice. Iterative deepening and recursive best-first search are the other two linear-space search algorithms. Iterative deepening is an important algorithm in Artificial Intelligence, and plays an irreplaceable role in building a real-time game-playing program.

About this title

Product Description

State Space Search Provides information on heuristic state-space search for combinatorial optimization, one of the fundamental problems of computer science. Written for researchers in computer science, this book presupposes a familiarity with complexity theory, assuming that the reader is familiar with the basic concepts of random variables and recursive functions.

Synopsis

This book is about problem-solving. In particular it is about heuristic state-space search for combinatorial optimization - one of the fundamental problems of computer science. Its two central themes are the average-case complexity of state-space search algorithms and the applications of the results notably to branch-and-bound techniques. These include best-first search, depth-first branch-and- bound, iterative deepening, recursive best-first search, and constant- space best-first search. Primarily written for researchers in computer science, the author presupposes a basic familiarity with complexity theory. In addition, it is assumed that the reader is familiar with the basic concepts of random variables and recursive functions. Two succesful applications are presented in depth: one is a set of state-space transformation methods which can be used to find approximate solutions qwuickly, and the second is a method called forward estimation for constructing more informative evaluation functions.

Edition details

• Publisher: Springer
• Publication date: 1999
• ISBN 10: 0387988327
• ISBN 13: 9780387988320
• Binding: Hardcover
• Language: English
• Number of pages: 217