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Academic literature on the topic 'Algorithm Backtracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Algorithm Backtracking"

1

Priestley, H. A., and M. P. Ward. "A Multipurpose Backtracking Algorithm." Journal of Symbolic Computation 18, no. 1 (1994): 1–40. http://dx.doi.org/10.1006/jsco.1994.1035.

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2

Van Beek, P., and D. W. Manchak. "The Design and Experimental Analysis of Algorithms for Temporal Reasoning." Journal of Artificial Intelligence Research 4 (January 1, 1996): 1–18. http://dx.doi.org/10.1613/jair.232.

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Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algor

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3

Gregorics, Tibor. "Object-oriented backtracking." Acta Universitatis Sapientiae, Informatica 9, no. 2 (2017): 144–61. http://dx.doi.org/10.1515/ausi-2017-0010.

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Abstract Several versions of the backtracking are known. In this paper, those versions are in focus which solve the problems whose problem space can be described with a special directed tree. The traversal strategies of this tree will be analyzed and they will be implemented in object-oriented style. In this way, the traversal is made by an enumerator object which iterates over all the paths (partial solutions) of the tree. Two different “acktracking enumerators” are going to be presented and the backtracking algorithm will be a linear search over one of these enumerators. Since these algorith

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4

Chen, X., and P. Van Beek. "Conflict-Directed Backjumping Revisited." Journal of Artificial Intelligence Research 14 (March 1, 2001): 53–81. http://dx.doi.org/10.1613/jair.788.

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In recent years, many improvements to backtracking algorithms for solving constraint satisfaction problems have been proposed. The techniques for improving backtracking algorithms can be conveniently classified as look-ahead schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes are not entirely orthogonal as it has been observed empirically that the enhancement of look-ahead techniques is sometimes counterproductive to the effects of look-back techniques. In this paper, we focus on the relationship between the two most important look-ahead techniques---using a variable

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5

CHOWDHURY, U., and D. K. GUPTA. "MODIFIED BACKJUMPING ALGORITHMS FOR SOLVING CONSTRAINT SATISFACTION PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 01 (1999): 133–47. http://dx.doi.org/10.1142/s0218001499000082.

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The backtracking algorithm is a prominent search technique in AI, particularly due to its use in Constraint Satisfaction Problems (CSPs), Truth Maintenance Systems (TMS), and PROLOG. In the context of CSPs, Dechter5 and Gashnig10 proposed two variants of the backtracking algorithm known as backjumping algorithms. One is graph-based and the other is failure-based backjumping algorithm. These algorithms attempt to backjump to the source of failure in case of a dead-end situation. This improves the backtracking performance. However, these algorithms are not consistent in the selection of the vari

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6

Ghanem, Khadoudja, and Abdesslem Layeb. "Feature Selection and Knapsack Problem Resolution Based on a Discrete Backtracking Optimization Algorithm." International Journal of Applied Evolutionary Computation 12, no. 2 (2021): 1–15. http://dx.doi.org/10.4018/ijaec.2021040101.

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Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objecti

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7

Janakiram, V. K., D. P. Agrawal, and R. Mehrotra. "A randomized parallel backtracking algorithm." IEEE Transactions on Computers 37, no. 12 (1988): 1665–76. http://dx.doi.org/10.1109/12.9745.

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8

Li, Hui, and Ping Hua Chen. "Improved Backtracking-Forward Algorithm for Maximum Matching Chinese Word Segmentation." Applied Mechanics and Materials 536-537 (April 2014): 403–6. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.403.

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In order to improve the accuracy of segmentation, analysis of backtracking-forward maximum matching algorithm exists two defects when dealing with crossing ambiguity, and on this basis, an improved-backtracking forward algorithm for maximum matching algorithm is presented. The improved algorithm is based on the backtracking-forward maximum matching algorithm and adds a module, a chain length of one and 3-words, that can detect and process crossing ambiguity, and taking advantage of counting method, we can merely sort out the defragmenter fields that occurred crossing ambiguity. A number of sel

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9

Li, Bingbing, Li Ding, Mark Rajai, Di Hu, and Shengzi Zheng. "Backtracking Algorithm-based Disassembly Sequence Planning." Procedia CIRP 69 (2018): 932–37. http://dx.doi.org/10.1016/j.procir.2017.12.007.

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10

Wang, Shijie, Guiling Sun, and Yangyang Li. "SVD++ Recommendation Algorithm Based on Backtracking." Information 11, no. 7 (2020): 369. http://dx.doi.org/10.3390/info11070369.

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Collaborative filtering (CF) has successfully achieved application in personalized recommendation systems. The singular value decomposition (SVD)++ algorithm is employed as an optimized SVD algorithm to enhance the accuracy of prediction by generating implicit feedback. However, the SVD++ algorithm is limited primarily by its low efficiency of calculation in the recommendation. To address this limitation of the algorithm, this study proposes a novel method to accelerate the computation of the SVD++ algorithm, which can help achieve more accurate recommendation results. The core of the proposed

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