Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
On the construction of heuristic functions
On the construction of heuristic functions
Average-case analysis of heuristic search in tree-like networks
Search in Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
Generating effective admissible heuristics by abstraction and reconstitution
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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In high-performance A* searching to solve satisficing problems, there is a critical need to design heuristics which cause low time-complexity. In order for humans or machines to do this effectively, there must be an understanding of the domain-independent properties that such heuristics have. We snow that, contrary to common belief, accuracy is not critical; the key issue is whether or not heuristic values are concentrated closely near a rapidly growing "central function." As an application, we show that, by "multiplying" heuristics, it is possible to reduce exponential average time-complexity to polynomial. This is contrary to conclusions drawn from previous studies. Experimental and theoretical examples are given.