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

Portada
Springer Science & Business Media, 6 de des. 2012 - 201 pàgines
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.
 

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Continguts

StateSpace Search for Problem Solving
1
Algorithms for Combinatorial Optimization
13
Complexity of StateSpace Search for Optimal Solutions
34
Computational Complexity Transitions
61
Algorithm Selection
83
A Study of BranchandBound on the Asymmetric Traveling
92
StateSpace Transformation for Approximation and Flexible
116
Forward Pruning for Approximation and Flexible Computation
144
Forward Pruning for Approximation and Flexible Computation
172
B Mathematical Notation
185
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