State-Space Search: Algorithms, Complexity, Extensions, and ApplicationsSpringer 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. |
Continguts
StateSpace Search for Problem Solving | 1 |
Algorithms for Combinatorial Optimization | 13 |
2 | 38 |
Computational Complexity Transitions | 61 |
3 | 84 |
2 | 87 |
16 | 99 |
2 | 104 |
34 | 140 |
8 | 143 |
3 | 149 |
Approximation based on branchandbound | 171 |
Forward Pruning for Approximation and Flexible Computation | 172 |
Complexity of StateSpace Search for Optimal Solutions | 177 |
5 | 181 |
References | 187 |
Altres edicions - Mostra-ho tot
State-Space Search: Algorithms, Complexity, Extensions, and Applications Weixiong Zhang Previsualització limitada - 1999 |
State-Space Search: Algorithms, Complexity, Extensions, and Applications Weixiong Zhang Previsualització no disponible - 2012 |
Frases i termes més freqüents
actual-value pruning alpha-beta pruning Artificial Intelligence assignment problem assignment problem solution asymmetric Traveling Salesman asymptotically optimal ATSP average number beam search branching factor branching process combinatorial optimization complete forward pruning complete tour complexity transitions cyclic permutations distinct intercity distances edge costs expected number exponential ɛ-transformation Figure find an optimal finding a goal heuristic pruning rules included arcs incremental random tree iterative deepening iterative ɛ-DFBnB leaf node Lemma log log log-normal distribution lower bound matrices maximum boolean 3-SAT minimax value node costs nodes expanded NP-hard number of cities number of distinct number of nodes optimal goal cost optimal goal node optimal solution optimal tour polynomial probability problem instances pruning algorithm random tree T(b recursive best-first search root node search depth search tree solution cost solution quality solved space subproblems subtree Theorem total number Traveling Salesman Problem truncated depth-first branch-and-bound uniformly chosen upper bound value of ɛ variables