A heuristic search algorithm with modifiable estimate
Artificial Intelligence
Depth-first iterative-deepening: an optimal admissible tree 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
Searching with Pattern Databases
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
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
An anytime algorithm for gapped block protein threading with pair interactions
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
A space-time tradeoff for memory-based heuristics
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Recent Progress in the Design and Analysis of Admissible Heuristic Functions
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Experiments with Automatically Created Memory-Based Heuristics
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Prediction of Regular Search Tree Growth by Spectral Analysis
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Memory-efficient A* heuristics for multiple sequence alignment
Eighteenth national conference on Artificial intelligence
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
On the value of good advice: the complexity of A* search with accurate heuristics
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Analyzing the performance of pattern database heuristics
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Predicting the performance of IDA* with conditional distributions
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
A survey and classification of A* based best-first heuristic search algorithms
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Inconsistent heuristics in theory and practice
Artificial Intelligence
Efficient memory bound puzzles using pattern databases
ACNS'06 Proceedings of the 4th international conference on Applied Cryptography and Network Security
The time complexity of A* with approximate heuristics on multiple-solution search spaces
Journal of Artificial Intelligence Research
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We analyze the asymptotic time complexity of admissible heuristic search algorithms such as A*, IDA*, and depth-first branch-and-bound. Previous analyses relied on an abstract analytical model, and characterize the heuristic function in terms of its accuracy, but do not apply to real problems. In contrast, our analysis allows us to accurately predict the performance of these algorithms on problems such as the slidingtile puzzles and Rubik's Cube. The heuristic function is characterized simply by the distribution of heuristic values in the problem space. Contrary to conventional wisdom, our analysis shows that the asymptotic heuristic branching factor is the same as the bruteforce branching factor, and that the effect of a heuristic function is to reduce the effective depth of search, rather than the effective branching factor.