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151,203
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 30 (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
Informed Backtracking Beam Search
"... Beam searches are a very effective algorithm for solving problems that prove intractable to complete algorithms like weighted A * and greedy search. Unfortunately, the inadmissible pruning that is the hallmark of a beam search makes the algorithm incomplete. Existing complete extensions to beam sea ..."
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Beam searches are a very effective algorithm for solving problems that prove intractable to complete algorithms like weighted A * and greedy search. Unfortunately, the inadmissible pruning that is the hallmark of a beam search makes the algorithm incomplete. Existing complete extensions to beam
Complete anytime beam search
 In Proc. AAAI 1998
, 1998
"... Beam search executes a search method, such as bestfirst search or depthfirst search, but may abandon nonpromising search avenues in order to reduce complexity. Although it has existed for more than two decades and has been applied to many realworld problems, beam search still suffers from the dra ..."
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Cited by 16 (3 self)
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Beam search executes a search method, such as bestfirst search or depthfirst search, but may abandon nonpromising search avenues in order to reduce complexity. Although it has existed for more than two decades and has been applied to many realworld problems, beam search still suffers from
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 32 (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
Language Modeling for Efficient BeamSearch
 Computer Speech and Language
, 1995
"... This paper considers the problems of estimating bigram language models and of efficiently representing them by a finite state network, which can be employed by an hidden Markov model based, beamsearch, continuous speech recognizer. ..."
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Cited by 5 (4 self)
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This paper considers the problems of estimating bigram language models and of efficiently representing them by a finite state network, which can be employed by an hidden Markov model based, beamsearch, continuous speech recognizer.
Beam search strategy for IPS: Integration of a structurebased beam search
, 1996
"... this paper the integration of a structurebased beam search algorithm into the IPS parser. The beam is used both as filtering and ranking device. The same strategy and the same data structure will serve for both tasks. This structurebased algorithm is the first 1 ..."
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this paper the integration of a structurebased beam search algorithm into the IPS parser. The beam is used both as filtering and ranking device. The same strategy and the same data structure will serve for both tasks. This structurebased algorithm is the first 1
Backward Beam Search Algorithm
 In Proceedings of the COLING 2000
, 2000
"... Backward beton search ibr dependency analysis of Japanese is proposed. As dependencies hornrally go h'om left to right in Japanese, it is effective to analyze sentences backwards (from right to left). The analysis is based on a statisti cal method and employs a beton search strategy. Based on ..."
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on experiments varying the beton search width, we ibund that the accnracy is not sensitive to the beam width and even the analysis with a beton width of 1 gets ahnost the stone dependency accuracy as the best accuracy using a wider beam width. This snggested a deterministic algorithm for backwards .lapanese
Optimal Beam Search for Machine Translation
"... Beam search is a fast and empirically effective method for translation decoding, but it lacks formal guarantees about search error. We develop a new decoding algorithm that combines the speed of beam search with the optimal certificate property of Lagrangian relaxation, and apply it to phrase and s ..."
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Cited by 1 (0 self)
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Beam search is a fast and empirically effective method for translation decoding, but it lacks formal guarantees about search error. We develop a new decoding algorithm that combines the speed of beam search with the optimal certificate property of Lagrangian relaxation, and apply it to phrase
Viterbi Beam Search with Layered Bigrams
, 1996
"... We outline an implementation of Viterbi beam search that incorporates layered bigrams. Layered bigrams are class bigrams in which some nodes are themselves bigrams, resulting in a recursive structure. The implementation is in C ++ and involves a hierarchy of classes. The paper outlines the main con ..."
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Cited by 1 (0 self)
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We outline an implementation of Viterbi beam search that incorporates layered bigrams. Layered bigrams are class bigrams in which some nodes are themselves bigrams, resulting in a recursive structure. The implementation is in C ++ and involves a hierarchy of classes. The paper outlines the main
Beam Search in Incremental Rule Learning
, 2003
"... This paper describes ICN, an incremental version of the CN2 rule learning system. Unlike other incremental rule learning systems which learn rules gradually, adding and removing conditions in a hillclimbing search, ICN learns or unlearns each rule "all at once," using beam search as in CN ..."
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This paper describes ICN, an incremental version of the CN2 rule learning system. Unlike other incremental rule learning systems which learn rules gradually, adding and removing conditions in a hillclimbing search, ICN learns or unlearns each rule "all at once," using beam search
Results 1  10
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151,203