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19
Heuristic Search
, 2011
"... Heuristic search is used to efficiently solve the singlenode shortest path problem in weighted graphs. In practice, however, one is not only interested in finding a short path, but an optimal path, according to a certain cost notion. We propose an algebraic formalism that captures many cost notions ..."
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Cited by 44 (22 self)
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Heuristic search is used to efficiently solve the singlenode shortest path problem in weighted graphs. In practice, however, one is not only interested in finding a short path, but an optimal path, according to a certain cost notion. We propose an algebraic formalism that captures many cost notions, like typical Quality of Service attributes. We thus generalize A*, the popular heuristic search algorithm, for solving optimalpath problem. The paper provides an answer to a fundamental question for AI search, namely to which general notion of cost, heuristic search algorithms can be applied. We proof correctness of the algorithms and provide experimental results that validate the feasibility of the approach.
Beamstack search: Integrating backtracking with beam search
 In International Conference on Automated Planning and Scheduling (ICAPS
, 2005
"... We describe a method for transforming beam search into a complete search algorithm that is guaranteed to find an optimal solution. Called beamstack search, the algorithm uses a new data structure, called a beam stack, that makes it possible to integrate systematic backtracking with beam search. The ..."
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Cited by 23 (3 self)
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We describe a method for transforming beam search into a complete search algorithm that is guaranteed to find an optimal solution. Called beamstack search, the algorithm uses a new data structure, called a beam stack, that makes it possible to integrate systematic backtracking with beam search. The resulting search algorithm is an anytime algorithm that finds a good, suboptimal solution quickly, like beam search, and then backtracks and continues to find improved solutions until convergence to an optimal solution. We describe a memoryefficient implementation of beamstack search, called divideandconquer beamstack search, as well as an iterativedeepening version of the algorithm. The approach is applied to domainindependent STRIPS planning, and computational results show its advantages.
Limited discrepancy beam search
 In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI
, 2005
"... Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memorybounded search method th ..."
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Cited by 20 (2 self)
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Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memorybounded search method that is able to solve more problem instances of large search problems than beam search and does so with a reasonable runtime. At the same time, BULB tends to find shorter paths than beam search because it is able to use larger beam widths without running out of memory. We demonstrate these properties of BULB experimentally for three standard benchmark domains. 1
Sweep A*: Spaceefficient heuristic search in partially ordered graphs
 In Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
, 2003
"... We describe a novel heuristic search algorithm, called Sweep A*, that exploits the regular structure of partially ordered graphs to substantially reduce the memory requirements of search. We show that it outperforms previous search algorithms in optimally aligning multiple protein or DNA sequences, ..."
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Cited by 19 (5 self)
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We describe a novel heuristic search algorithm, called Sweep A*, that exploits the regular structure of partially ordered graphs to substantially reduce the memory requirements of search. We show that it outperforms previous search algorithms in optimally aligning multiple protein or DNA sequences, an important problem in bioinformatics. Sweep A * also promises to be effective for other search problems with similar structure. 1.
An improved search algorithm for optimal multiplesequence alignment
 Journal of Artificial Intelligence Research
, 2005
"... Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NPhard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be c ..."
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Cited by 6 (0 self)
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Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NPhard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschberg’s algorithm, Dynamic Programming needs O(kN k−1) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from reexpanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Closed
Evaluations of Hash Distributed A * in Optimal Sequence Alignment
 PROCEEDINGS OF THE TWENTYSECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Hash Distributed A* (HDA*) is a parallel A* algorithm that is proven to be effective in optimal sequential planning with unit edge costs. HDA* leverages the Zobrist function to almost uniformly distribute and schedule work among processors. This paper evaluates the performance of HDA * in optimal se ..."
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Cited by 1 (0 self)
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Hash Distributed A* (HDA*) is a parallel A* algorithm that is proven to be effective in optimal sequential planning with unit edge costs. HDA* leverages the Zobrist function to almost uniformly distribute and schedule work among processors. This paper evaluates the performance of HDA * in optimal sequence alignment. We observe that with a large number of CPU cores HDA* suffers from an increase of search overhead caused by reexpansions of states in the closed list due to nonuniform edge costs in this domain. We therefore present a new work distribution strategy limiting processors to distribute work, thus increasing the possibility of detecting such duplicate search effort. We evaluate the performance of this approach on a cluster of multicore machines and show that the approach scales well up to 384 CPU cores.
Kgroup A* for multiple sequence alignment with quasinatural gap costs
 In Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI04
, 2004
"... Alignment of multiple protein or DNA sequences is an important problem in Bioinformatics. Previous work has shown that the A * search algorithm can find optimal alignments for up to several sequences, and that a Kgroup generalization of A * can find approximate alignments for much larger numbers of ..."
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Alignment of multiple protein or DNA sequences is an important problem in Bioinformatics. Previous work has shown that the A * search algorithm can find optimal alignments for up to several sequences, and that a Kgroup generalization of A * can find approximate alignments for much larger numbers of sequences [6]. In this paper, we describe the first implementation of Kgroup A * that uses quasinatural gap costs, the cost model used in practice by biologists. We also introduce a new method for computing gapopening costs in profile alignment. Our results show that Kgroup A * can efficiently find optimal or closetooptimal alignments for small groups of sequences, and, for large numbers of sequences, it can find higherquality alignments than the widelyused CLUSTAL family of approximate alignment tools. This demonstrates the benefits of A* in aligning large numbers of sequences, as typically compared by biologists, and suggests that Kgroup A * could become a practical tool for multiple sequence alignment. 1.
BFHSP: A breadthfirst heuristic search planner
 In 4th International Planning Competition Booklet, 61 – 63. Available at http://ipc.icapsconference.org
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