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Journal articles on the topic 'Approximate Decision'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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1

Schirra, Stefan. "Approximate decision algorithms for approximate congruence." Information Processing Letters 43, no. 1 (August 1992): 29–34. http://dx.doi.org/10.1016/0020-0190(92)90025-q.

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2

Syau, Yu-Ru, En-Bing Lin, and Lixing Jia. "Discernibility Thresholds and Approximate Dependency in Analysis of Decision Tables." Journal of Computers 10, no. 6 (November 2015): 412–17. http://dx.doi.org/10.17706/jcp.10.6.412-417.

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3

Kuśmierczyk, Tomasz, Joseph Sakaya, and Arto Klami. "Correcting Predictions for Approximate Bayesian Inference." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4511–18. http://dx.doi.org/10.1609/aaai.v34i04.5879.

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Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.
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4

Dearden, Richard, and Craig Boutilier. "Abstraction and approximate decision-theoretic planning." Artificial Intelligence 89, no. 1-2 (January 1997): 219–83. http://dx.doi.org/10.1016/s0004-3702(96)00023-9.

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5

Ulieru, Mihaela. "Approximate Reasoning Approaches for Diagnostic Decision." IFAC Proceedings Volumes 27, no. 5 (June 1994): 339–44. http://dx.doi.org/10.1016/s1474-6670(17)48050-5.

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6

Heffernan, Paul J., and Stefan Schirra. "Approximate decision algorithms for point set congruence." Computational Geometry 4, no. 3 (July 1994): 137–56. http://dx.doi.org/10.1016/0925-7721(94)90004-3.

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7

Jayawardena, Chandimal, Keigo Watanabe, and Kiyotaka Izumi. "Learning from Approximate Human Decisions by a Robot." Journal of Robotics and Mechatronics 19, no. 1 (February 20, 2007): 68–76. http://dx.doi.org/10.20965/jrm.2007.p0068.

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Robot systems operating under natural-language commands must be able to infer the meaning intended by the issuer. Despite some successful research, however, an important related aspect not yet addressed has been the possibility of learning from natural-language commands. Such commands, generated by human users, contain valuable information. The inherent subjectivity of natural language, however, complicates potential learning from such commands and their interpretation. We propose decision making for robots operating under natural-language commands influenced by human aspects of decision making. Under our proposed concept, demonstrated in experiments conducted using a robotic manipulator, the robot is controlled using natural-language commands to conduct pick-and-place operations, during which the robot builds a knowledge base. After this learning, which uses a probabilistic neural network, the robot conducts similar tasks based on approximate decisions from the knowledge gained.
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8

Li, Mengmeng, Chiping Zhang, Minghao Chen, and Weihua Xu. "On local multigranulation covering decision-theoretic rough sets." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11107–30. http://dx.doi.org/10.3233/jifs-202274.

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Multi-granulation decision-theoretic rough sets uses the granular structures induced by multiple binary relations to approximate the target concept, which can get a more accurate description of the approximate space. However, Multi-granulation decision-theoretic rough sets is very time-consuming to calculate the approximate value of the target set. Local rough sets not only inherits the advantages of classical rough set in dealing with imprecise, fuzzy and uncertain data, but also breaks through the limitation that classical rough set needs a lot of labeled data. In this paper, in order to make full use of the advantage of computational efficiency of local rough sets and the ability of more accurate approximation space description of multi-granulation decision-theoretic rough sets, we propose to combine the local rough sets and the multigranulation decision-theoretic rough sets in the covering approximation space to obtain the local multigranulation covering decision-theoretic rough sets model. This provides an effective tool for discovering knowledge and making decisions in relation to large data sets. We first propose four types of local multigranulation covering decision-theoretic rough sets models in covering approximation space, where a target concept is approximated by employing the maximal or minimal descriptors of objects. Moreover, some important properties and decision rules are studied. Meanwhile, we explore the reduction among the four types of models. Furthermore, we discuss the relationships of the proposed models and other representative models. Finally, illustrative case of medical diagnosis is given to explain and evaluate the advantage of local multigranulation covering decision-theoretic rough sets model.
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9

Khaoula, Boutouhami, and Khellaf Faiza. "Optimistic Decision Making using an Approximate Graphical Model." International Journal of Artificial Intelligence & Applications 6, no. 2 (March 31, 2015): 1–20. http://dx.doi.org/10.5121/ijaia.2015.6201.

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10

Jin, Shangzhu, Jun Peng, Zuojin Li, and Qiang Shen. "Bidirectional approximate reasoning-based approach for decision support." Information Sciences 506 (January 2020): 99–112. http://dx.doi.org/10.1016/j.ins.2019.08.019.

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11

Sieling, Detlef. "Minimization of decision trees is hard to approximate." Journal of Computer and System Sciences 74, no. 3 (May 2008): 394–403. http://dx.doi.org/10.1016/j.jcss.2007.06.014.

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12

Dell, Holger, and John Lapinskas. "Fine-Grained Reductions from Approximate Counting to Decision." ACM Transactions on Computation Theory 13, no. 2 (June 2021): 1–24. http://dx.doi.org/10.1145/3442352.

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In this article, we introduce a general framework for fine-grained reductions of approximate counting problems to their decision versions. (Thus, we use an oracle that decides whether any witness exists to multiplicatively approximate the number of witnesses with minimal overhead.) This mirrors a foundational result of Sipser (STOC 1983) and Stockmeyer (SICOMP 1985) in the polynomial-time setting, and a similar result of Müller (IWPEC 2006) in the FPT setting. Using our framework, we obtain such reductions for some of the most important problems in fine-grained complexity: the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path). While all these problems have simple algorithms over which it is conjectured that no polynomial improvement is possible, our reductions would remain interesting even if these conjectures were proved; they have only polylogarithmic overhead and can therefore be applied to subpolynomial improvements such as the n 3 / exp(Θ (√ log n ))-time algorithm for the Negative-Weight Triangle problem due to Williams (STOC 2014). Our framework is also general enough to apply to versions of the problems for which more efficient algorithms are known. For example, the Orthogonal Vectors problem over GF( m ) d for constant m can be solved in time n · poly ( d ) by a result of Williams and Yu (SODA 2014); our result implies that we can approximately count the number of orthogonal pairs with essentially the same running time. We also provide a fine-grained reduction from approximate #SAT to SAT. Suppose the Strong Exponential Time Hypothesis (SETH) is false, so that for some 1 < c < 2 and all k there is an O ( c n )-time algorithm for k -SAT. Then we prove that for all k , there is an O (( c + o (1)) n )-time algorithm for approximate # k -SAT. In particular, our result implies that the Exponential Time Hypothesis (ETH) is equivalent to the seemingly weaker statement that there is no algorithm to approximate #3-SAT to within a factor of 1+ɛ in time 2 o ( n )/ ɛ 2 (taking ɛ > 0 as part of the input).
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13

Yu, Pengqian, William B. Haskell, and Huan Xu. "Approximate Value Iteration for Risk-Aware Markov Decision Processes." IEEE Transactions on Automatic Control 63, no. 9 (September 2018): 3135–42. http://dx.doi.org/10.1109/tac.2018.2790261.

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14

Ackermann, Heiner, Alantha Newman, Heiko Röglin, and Berthold Vöcking. "Decision-making based on approximate and smoothed Pareto curves." Theoretical Computer Science 378, no. 3 (June 2007): 253–70. http://dx.doi.org/10.1016/j.tcs.2007.02.034.

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15

Ciardo, Gianfranco. "Decision diagrams for the approximate analysis of Markov models." PAMM 7, no. 1 (December 2007): 1080705–6. http://dx.doi.org/10.1002/pamm.200700833.

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16

Canbolat, Pelin G., and Uriel G. Rothblum. "(Approximate) iterated successive approximations algorithm for sequential decision processes." Annals of Operations Research 208, no. 1 (February 8, 2012): 309–20. http://dx.doi.org/10.1007/s10479-012-1073-x.

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17

Yu, Jianhang, Biao Zhang, Minghao Chen, and Weihua Xu. "Double-quantitative decision-theoretic approach to multigranulation approximate space." International Journal of Approximate Reasoning 98 (July 2018): 236–58. http://dx.doi.org/10.1016/j.ijar.2018.05.001.

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18

Lun, Yuriy Zacchia, Jack Wheatley, Alessandro D’Innocenzo, and Alessandro Abate. "Approximate Abstractions of Markov Chains with Interval Decision Processes." IFAC-PapersOnLine 51, no. 16 (2018): 91–96. http://dx.doi.org/10.1016/j.ifacol.2018.08.016.

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19

Lassaigne, Richard, and Sylvain Peyronnet. "Approximate planning and verification for large Markov decision processes." International Journal on Software Tools for Technology Transfer 17, no. 4 (August 29, 2014): 457–67. http://dx.doi.org/10.1007/s10009-014-0344-z.

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20

Amin, Talha, Igor Chikalov, Mikhail Moshkov, and Beata Zielosko. "Dynamic programming approach to optimization of approximate decision rules." Information Sciences 221 (February 2013): 403–18. http://dx.doi.org/10.1016/j.ins.2012.09.018.

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21

Azad, Mohammad, Igor Chikalov, Mikhail Moshkov, and Beata Zielosko. "Greedy Algorithms for Construction of Approximate Tests for Decision Tables with Many-Valued Decisions." Fundamenta Informaticae 120, no. 3-4 (2012): 231–42. http://dx.doi.org/10.3233/fi-2012-759.

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22

Di Tria, Francesco, Ezio Lefons, and Filippo Tangorra. "Benchmark for Approximate Query Answering Systems." Journal of Database Management 26, no. 1 (January 2015): 1–29. http://dx.doi.org/10.4018/jdm.2015010101.

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The standard benchmark for Decision Support Systems is TPC-H, which is composed of a database, a workload, and a set of metrics for the performance evaluation. However, TPC-H does not include a methodology for the benchmark of Approximate Query Answering Systems, or the software tools used to obtain fast answers to analytical queries in the decision making process. In the paper, the authors present a methodology to evaluate and compare Approximate Query Answering Systems. To this aim, a methodology that extends the standard TPC-H and a set of new metrics that take into account the specific features of these systems are proposed. Experimental results show the application of these metrics to two systems based on the data analytic approximation by orthonormal series.
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23

Logé, Frédéric, Erwan Le Pennec, and Habiboulaye Amadou-Boubacar. "Intelligent Questionnaires Using Approximate Dynamic Programming." i-com 19, no. 3 (December 1, 2020): 227–37. http://dx.doi.org/10.1515/icom-2020-0022.

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Abstract Inefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum q questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given. The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy. This setting is quite flexible and can incorporate easily initial available data and grouped questions.
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24

Liu, Xin, Ying Wang, Xiaonan Li, and Li Zou. "A Linguistic-Valued Approximate Reasoning Approach for Financial Decision Making." International Journal of Computational Intelligence Systems 10, no. 1 (2017): 312. http://dx.doi.org/10.2991/ijcis.2017.10.1.21.

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25

Lakshminarayanan, Chandrashekar, Shalabh Bhatnagar, and Csaba Szepesvari. "A Linearly Relaxed Approximate Linear Program for Markov Decision Processes." IEEE Transactions on Automatic Control 63, no. 4 (April 2018): 1185–91. http://dx.doi.org/10.1109/tac.2017.2743163.

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26

Ahn, Byeong Seok. "Approximate weighting method for multiattribute decision problems with imprecise parameters." Omega 72 (October 2017): 87–95. http://dx.doi.org/10.1016/j.omega.2016.11.006.

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27

Du, Wen Sheng, and Bao Qing Hu. "Approximate distribution reducts in inconsistent interval-valued ordered decision tables." Information Sciences 271 (July 2014): 93–114. http://dx.doi.org/10.1016/j.ins.2014.02.070.

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28

Hazen, Gordon B., and Min Huang. "Parametric Sensitivity Analysis Using Large-Sample Approximate Bayesian Posterior Distributions." Decision Analysis 3, no. 4 (December 2006): 208–19. http://dx.doi.org/10.1287/deca.1060.0078.

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29

Iwaki, Hideki, Masaaki Kijima, and Toshihiro Yoshida. "Approximate valuation of average options." Annals of Operations Research 45, no. 1 (December 1993): 131–45. http://dx.doi.org/10.1007/bf02282045.

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30

GAO, YA, GUANGQUAN ZHANG, JIE LU, THARAM DILLON, and XIANYI ZENG. "A λ-CUT APPROXIMATE ALGORITHM FOR GOAL-BASED BILEVEL RISK MANAGEMENT SYSTEMS." International Journal of Information Technology & Decision Making 07, no. 04 (December 2008): 589–610. http://dx.doi.org/10.1142/s0219622008003113.

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Bilevel programming techniques are developed for decentralized decision problems with decision makers located in two levels. Both upper and lower decision makers, termed as leader and follower, try to optimize their own objectives in solution procedure but are affected by those of the other levels. When a bilevel decision model is built with fuzzy coefficients and the leader and/or follower have goals for their objectives, we call it fuzzy goal bilevel (FGBL) decision problem. This paper first proposes a λ-cut set based FGBL model. A programmable λ-cut approximate algorithm is then presented in detail. Based on this algorithm, a FGBL software system is developed to reach solutions for FGBL decision problems. Finally, two examples are given to illustrate the application of the proposed algorithm.
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31

Lin, Yong, Xingjia Lu, and Fillia Makedon. "Approximate Planning in POMDPs with Weighted Graph Models." International Journal on Artificial Intelligence Tools 24, no. 04 (August 2015): 1550014. http://dx.doi.org/10.1142/s0218213015500141.

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Markov decision process (MDP) based heuristic algorithms have been considered as simple, fast, but imprecise solutions for partially observable Markov decision processes (POMDPs). The main reason comes from how we approximate belief points. We use weighted graphs to model the state space and the belief space, in order for a detailed analysis of the MDP heuristic algorithm. As a result, we provide the prerequisite conditions to build up a robust belief graph. We further introduce a dynamic mechanism to manage belief space in the belief graph, so as to improve the efficiency and decrease the space complexity. Experimental results indicate our approach is fast and has high quality for POMDPs.
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32

Tria, Francesco Di, Ezio Lefons, and Filippo Tangorra. "System Architecture for Approximate Query Processing." Computer and Information Science 9, no. 2 (May 2, 2016): 156. http://dx.doi.org/10.5539/cis.v9n2p156.

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<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US"><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Decision making is an activity that addresses the problem of extracting knowledge and information from data stored in data warehouses, in order to improve the business processes of information systems. Usually, decision making is based on On-Line Analytical Processing, data mining, or approximate query processing. In the last case, answers to analytical queries are provided in a fast manner, although affected with a small percentage of error. In the paper, we present the architecture of an approximate query answering system. Then, we illustrate our ADAP (Analytical Data Profile) system, which is based on an engine able to provide fast responses to the main statistical functions by using orthogonal polynomials series to approximate the data distribution of multi­dimensional relations. Moreover, several experimental results to measure the approximation error are shown and the response-time to analytical queries is reported.</span></span></p>
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33

XIAO, Xiang, Xiang ZHANG, Haipeng WANG, Hongbin SUO, Qingwei ZHAO, and Yonghong YAN. "Approximate Decision Function and Optimization for GMM-UBM Based Speaker Verification." IEICE Transactions on Information and Systems E92-D, no. 9 (2009): 1798–802. http://dx.doi.org/10.1587/transinf.e92.d.1798.

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34

Chang, Hyeong Soo, and Steven I. Marcus. "Approximate receding horizon approach for Markov decision processes: average reward case." Journal of Mathematical Analysis and Applications 286, no. 2 (October 2003): 636–51. http://dx.doi.org/10.1016/s0022-247x(03)00506-7.

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35

Hashemi, Ray R., Fred Choobineh, John Talburt, William Slikker, and Merle G. Paule. "A diagnostic system based on a multi-decision approximate rules model." ACM SIGBIO Newsletter 17, no. 1 (April 1997): 1–8. http://dx.doi.org/10.1145/263239.263241.

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36

Chun, Myung-Geun. "A similarity-based bidirectional approximate reasoning method for decision-making systems." Fuzzy Sets and Systems 117, no. 2 (January 2001): 269–78. http://dx.doi.org/10.1016/s0165-0114(99)00093-7.

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37

Lingyang Song, R. C. de Lamare, A. Hjorungnes, and A. G. Burr. "Approximate ML Decision-Feedback Block Equalizer for Doubly Selective Fading Channels." IEEE Transactions on Vehicular Technology 58, no. 5 (2009): 2314–21. http://dx.doi.org/10.1109/tvt.2008.2009758.

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38

Yoneda, Kiyoshi, and Walter Celaschi. "A Utility Function to Solve Approximate Linear Equations for Decision Making." Decision Making in Manufacturing and Services 7, no. 1-2 (2013): 5. http://dx.doi.org/10.7494/dmms.2013.7.1.5.

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39

Li, Jinhai, Changlin Mei, and Yuejin Lv. "Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction." International Journal of Approximate Reasoning 54, no. 1 (January 2013): 149–65. http://dx.doi.org/10.1016/j.ijar.2012.07.005.

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40

Zhang, Jian, Mahjoub Dridi, and Abdellah El Moudni. "Scheduling Elective Surgeries with Markov Decision Process and Approximate Dynamic Programming." IFAC-PapersOnLine 52, no. 13 (2019): 1831–36. http://dx.doi.org/10.1016/j.ifacol.2019.11.468.

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41

Liu, Fang, Xu Tan, Hui Yang, and Hui Zhao. "Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4041–58. http://dx.doi.org/10.3233/jifs-200200.

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Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.
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42

Yang, Zhenhua, and Bingsheng He. "A Relaxed Approximate Proximal Point Algorithm." Annals of Operations Research 133, no. 1-4 (January 2005): 119–25. http://dx.doi.org/10.1007/s10479-004-5027-9.

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43

Dimnaku, Artan, Rex Kincaid, and Michael W. Trosset. "Approximate Solutions of Continuous Dispersion Problems." Annals of Operations Research 136, no. 1 (April 2005): 65–80. http://dx.doi.org/10.1007/s10479-005-2039-z.

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44

Lee, Su-Jin, Young-Jin Park, and Han-Lim Choi. "Efficient sensor network planning based on approximate potential games." International Journal of Distributed Sensor Networks 14, no. 6 (June 2018): 155014771878145. http://dx.doi.org/10.1177/1550147718781454.

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This article addresses information-based sensing point selection from a set of possible sensing locations. A potential game approach has been applied to addressing distributed decision making for cooperative sensor planning. For a large sensor network, the local utility function for an agent is difficult to compute, because the utility function depends on the other agents’ decisions, while each sensing agent is inherently faced with limitations in both its communication and computational capabilities. Accordingly, we propose an approximation method for a local utility function to accommodate limitations in information gathering and processing, using only a part of the decisions of other agents. The error induced by the approximation is also analyzed, and to keep the error small, we propose a selection algorithm that chooses the neighbor set for each agent in a greedy way. The selection algorithm is based on the correlation between one agent’s and the other agents’ measurement selection. Furthermore, we show that a game with an approximate utility function has an [Formula: see text]equilibrium and the set of the equilibria include the Nash equilibrium of the original potential game. We demonstrate the validity of our approximation method through two numerical examples on simplified weather forecasting and multi-target tracking.
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45

Dai, Li Yun. "An Approximate Formula for WER of TURBO-Like Codes over Fading Channels." Applied Mechanics and Materials 263-266 (December 2012): 1134–37. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1134.

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By using radius of decision region, an approximate formula to estimate word error rate (WER) of Turbo-like codes is proposed. The approximate formula is simply expressed in the form of Gaussian Q function, which consists of signal to noise ratio (SNR), mathematical expectation and variance of squared radii in decision region, etc. In addition, the approximate expression can be easily extended to fading channels with negligible error of WER. Simulation results show that the deviation between the proposed formula and Monte Carlo simulation is no more than 1dB over fading channels.
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46

Huys, Quentin J. M., Níall Lally, Paul Faulkner, Neir Eshel, Erich Seifritz, Samuel J. Gershman, Peter Dayan, and Jonathan P. Roiser. "Interplay of approximate planning strategies." Proceedings of the National Academy of Sciences 112, no. 10 (February 9, 2015): 3098–103. http://dx.doi.org/10.1073/pnas.1414219112.

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Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or “options.”
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47

Hernández Castellanos, Carlos Ignacio, Oliver Schütze, Jian-Qiao Sun, and Sina Ober-Blöbaum. "Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions." Mathematical and Computational Applications 25, no. 1 (January 8, 2020): 3. http://dx.doi.org/10.3390/mca25010003.

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In this paper, we present a novel evolutionary algorithm for the computation of approximate solutions for multi-objective optimization problems. These solutions are of particular interest to the decision-maker as backup solutions since they can provide solutions with similar quality but in different regions of the decision space. The novel algorithm uses a subpopulation approach to put pressure towards the Pareto front while exploring promissory areas for approximate solutions. Furthermore, the algorithm uses an external archiver to maintain a suitable representation in both decision and objective space. The novel algorithm is capable of computing an approximation of the set of interest with good quality in terms of the averaged Hausdorff distance. We underline the statements on some academic problems from literature and an application in non-uniform beams.
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48

Di Tria, Francesco, Ezio Lefons, and Filippo Tangorra. "Metadata for Approximate Query Answering Systems." Advances in Software Engineering 2012 (September 3, 2012): 1–13. http://dx.doi.org/10.1155/2012/247592.

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In business intelligence systems, data warehouse metadata management and representation are getting more and more attention by vendors and designers. The standard language for the data warehouse metadata representation is the Common Warehouse Metamodel. However, business intelligence systems include also approximate query answering systems, since these software tools provide fast responses for decision making on the basis of approximate query processing. Currently, the standard meta-model does not allow to represent the metadata needed by approximate query answering systems. In this paper, we propose an extension of the standard metamodel, in order to define the metadata to be used in online approximate analytical processing. These metadata have been successfully adopted in ADAP, a web-based approximate query answering system that creates and uses statistical data profiles.
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49

Flåm, Sjur D., and Alain Fougères. "Infinite horizon programs; convergence of approximate solutions." Annals of Operations Research 29, no. 1 (December 1991): 333–50. http://dx.doi.org/10.1007/bf02283604.

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50

Scherer, William T., Thomas A. Pomroy, and Douglas N. Fuller. "The triangular density to approximate the normal density: decision rules-of-thumb." Reliability Engineering & System Safety 82, no. 3 (December 2003): 331–41. http://dx.doi.org/10.1016/j.ress.2003.08.003.

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