Search Methods for Artificial IntelligenceSurveys a variety of search methods for problem-solving in terms of the sorts of problems that arise in the development of artificial intelligence. The theoretical and practical implications of a number of applications are examined (gathering, storing and processing systems and more). |
Contents
Introduction | 3 |
Problem Representation and Search Principles | 32 |
Brute Force Strategies | 56 |
Copyright | |
11 other sections not shown
Common terms and phrases
activation function analysis AND/OR graphs Artificial Intelligence assigned backtracking algorithm best-first strategy Bolc branching factor chromosomes components convergence cost function cross-over operator Cytowski Dechter defined delete denotes determined edge consistency element empty OPEN evaluating examined nodes execution figure finite fitness function game tree genetic algorithm genetic operators goal node heuristic estimate heuristic function hypergraph initial node iterative deepening Kanal Korf Kumar leaf level node matching member nprim methods minimal minimal spanning tree minimax minimax algorithm network of constraints Neural Networks nida nodes examined nprim->g number of nodes OPEN1 optimal solution p->decnot p->next parameters Pearl 84 player Proc procedure processors programs pruning algorithm queens problem representation return NULL robotics satisfied schema scout algorithm search algorithms selection solution path solution tree speech recognition ẞ pruning subprogrammes successors tree search algorithm V₁ variable