Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
A complete anytime algorithm for number partitioning
Artificial Intelligence
A high-performance exact method for the resource-constrained project scheduling problem
Computers and Operations Research
An Admissible Heuristic Search Algorithm
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
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This paper presents aneffective near-optimal search method forstate-space problems. The method, LTAast(Learning Threshold Aast), accepts athreshold parameter, p, as an input and finds asolution within that range of the optimum. Thelarger the parameter, the faster the methodfinds a solution. LTAast is based on acombination of recursion and dynamic memoryand, like Aast, keeps information about allstates in memory. In contrast to Aasthowever, which represents each node as acomplete state, LTAast represents each nodeusing an operator. This representation of thenodes makes LTAast dramatically efficientwith respect to memory usage. Another advantageof LTAast is that it eliminates any need forcomputational effort to maintain a priorityqueue, and this elimination significantlyincreases speed. To test the effectiveness andefficiency of the method we have applied it toNP-hard problems in scheduling. The testresults indicate that the method is effectivein trading speed with the quality of solutionsand that it is efficient in producing solutionsfor p = 0.