Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Average-case analysis of heuristic search in tree-like networks
Search in Artificial Intelligence
Cost-error relationships in A* tree-searching
Journal of the ACM (JACM)
Performance of linear-space search algorithms
Artificial Intelligence
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Divide-and-Conquer Frontier Search Applied to Optimal Sequence Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
How to use limited memory in heuristic search
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Searching graphs with A*: applications to job sequencing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Average-case analysis of best-first search in two representative directed acyclic graphs
Artificial Intelligence
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Many problems in real-world applications require searching graphs. Understanding the performance of search algorithms has been one of the eminent tasks of heuristic search research. Despite the importance of graph search algorithms, the research of analyzing their performance is limited, and most work on search algorithm analysis has been focused on tree search algorithms. One of the major obstacles to analyzing graph search is that no single graph is an appropriate representative of graph search problems. In this paper, we propose one possible approach to analyzing graph search: Analyzing the performance of graph search algorithms on a representative graph of a cluster of problems. We specifically consider job-sequencing problems in which a set of jobs must be sequenced on a machine such that a penalty function is minimized. We analyze the performance of A* graph search algorithm on an abstract model that closely represents job sequencing problems. It is an extension to a model widely used previously for analyzing tree search. One of the main results of our analysis is the existence of a gap of computational cost between two classes of job sequencing problems, one with exponential and the other with polynomial complexity. We provide experimental results showing that real job sequencing problems indeed have a huge difference on computational costs under different conditions.