Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
The branching factor of regular search spaces
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Complexity analysis admissible heuristic search
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Disjoint pattern database heuristics
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Pushing the limits: new developments in single-agent search
Pushing the limits: new developments in single-agent search
Finding optimal solutions to the twenty-four puzzle
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Algorithms for memory hierarchies: advanced lectures
Algorithms for memory hierarchies: advanced lectures
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
The time complexity of A* with approximate heuristics on multiple-solution search spaces
Journal of Artificial Intelligence Research
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The time complexity analysis of the IDA* algorithm has shown that predicting the growth of the search tree essentially relies on only two criteria: The number of nodes in the brute-force search tree for a given depth and the equilibrium distribution of the heuristicestimate. Since the latter can be approximated by random sampling, we accurately predict the number of nodes in the brute-force search tree for large depth in closed form by analyzing the spectrum of the problem graph or one of its factorization.We further derive that the asymptotic brute-force branching factor is in fact the spectral radius of the problem graph and exemplify our considerations in the domain of the (n2 - 1)-Puzzle.