Search in artificial intelligence
This book brings together some new insights and recent developments on the topics of search procedures in Artificial Intelligence and the relationships among search methods in Artificial Intelligence, Operations Research, and Engineering. The purpose of the book is to present these new insights and recent developments in a manner accessible to students and professionals in Computer Science, Engineering, Operations Research, and Applied Mathematics. The articles should provide the reader with a broad view of recent developments on search in AI and some of the relationships among branch and bound, heuristic search, and dynamic programming. New models for discrete optimization problems, new results on the average case of complexity of the well known A* algorithm, new results on the conditions under which A* is optimal over other search algorithms, use of different sources of knowledge in heuristic search, new results on the constraint satisfaction problem, and a result showing the minimax back up rule does not do as well as the product rule in some real games.
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An Algebra for Search Problems and Their Solutions
A General BranchandBound Formulation
AverageCase Analysis of Heuristic Search
9 other sections not shown
admissible AND/OR graph arc consistency Artificial Intelligence B&B procedure best-first binary branch and bound branching factor breadth-first search CFDR checks complexity components computation traces consider constraint satisfaction problems corresponding cost function Dechter defined definition depth-first search domain dynamic programming Eight Puzzle empty-domain evaluation function example expanded exponential Figure formulation g-queens game tree given goal node heuristic estimate heuristic search implicit enumeration algorithms instantiation lateral combination Lemma Lookahead lower bound macro network minimax monotone network of constraints node expansions number of nodes optimal solution ordered constraint graph pair parse tree partial tree performance problem instance problem type Proof represented RFL1 rooted Rubik's Cube search algorithms search problem search procedures search tree Section selection sequence serializable solution path solution space solution tree strategy subgoals subgraph subset subtree terminal Theorem tree search vertex vertical combinations vertical search width