Complexity analysis admissible heuristic search
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine Discovery of Effective Admissible Heuristics
Machine Learning
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
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
Hierarchical A *: searching abstraction hierarchies efficiently
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Admissible Moves in Two-Player Games
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Searching for Macro Operators with Automatically Generated Heuristics
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Maximizing over multiple pattern databases speeds up heuristic search
Artificial Intelligence
The computational complexity of avoiding spurious states in state space abstraction
Artificial Intelligence
Solving the 24 puzzle with instance dependent pattern databases
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Efficient memory bound puzzles using pattern databases
ACNS'06 Proceedings of the 4th international conference on Applied Cryptography and Network Security
| Hi-index | 0.00 |
A memory-based heuristic is a function, h(s), stored in the form of a lookup table: h(s) is computed by mapping s to an index and then retrieving the corresponding entry in the table. In this paper we present a notation for describing state spaces, PSVN, anda method for automatically creating memory-based heuristics for a state space by abstracting its PSVN description. Two investigations of these automatically generated heuristics are presented. First, thousands of automatically generated heuristics are used to experimentally investigate the conjecture by Korf [4] that m ċ t is a constant, where m is the size of a heuristic's lookup table and t is the number of nodes expanded when the heuristic is used to guide search. Second, a similar large-scale experiment is used to verify that the Korf and Reid's complexity analysis [5] can be used to rapidly and reliably choose the best among a given set of heuristics.