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Statespace Search: Algorithms, Complexity, Extensions, And Applications
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This book is about problem solving. Specifically, it is about heuristic statespace search under branchandbound framework for solving com binatorial optimization problems. The two central themes of this book are the averagecase complexity of heuristic statespace search algorithms based on branchandbound, and their applications to developing new problemsolving methods and algorithms. Heuristic statespace search is one of the fundamental problemsolving techniques in Computer Science and Operations Research, and usually constitutes an important component of most intelligent problemsolving systems. The search algorithms considered in this book can be classified into the category of branchandbound. Branchandbound is a general problemsolving paradigm, and is one of the best techniques for optimally solving computationintensive problems, such as scheduling and planning. The main search algorithms considered include bestfirst search, depth first branchandbound, iterative deepening, recursive bestfirst search, and spacebounded bestfirst search. Bestfirst search and depthfirst branchandbound are very well known and have been used extensively in Computer Science and Operations Research. One important feature of depthfirst branchandbound is that it only requires space this is linear in the maximal search depth, making it very often a favorable search algo rithm over bestfirst search in practice. Iterative deepening and recursive bestfirst search are the other two linearspace search algorithms. Iterative deepening is an important algorithm in Artificial Intelligence, and plays an irreplaceable role in building a realtime gameplaying program.
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