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Journal articles on the topic 'Problem-solving space'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Glazkov, V. V., O. A. Sinkevich, and G. B. Shmelkov. "Universal Rocket Space Engine and solving the problem of space debris." Journal of Physics: Conference Series 1370 (November 2019): 012036. http://dx.doi.org/10.1088/1742-6596/1370/1/012036.

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2

Appleton, Ken. "Problem solving in science lessons: How students explore the problem space." Research in Science Education 25, no. 4 (1995): 383–93. http://dx.doi.org/10.1007/bf02357384.

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3

Polat, Faruk, and Reda Alhajj. "A multi-agent tuple-space based problem solving framework." Journal of Systems and Software 47, no. 1 (1999): 11–17. http://dx.doi.org/10.1016/s0164-1212(99)00019-9.

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4

Cybulski, Jacob L., Susan Keller, Lemai Nguyen, and Dilal Saundage. "Creative problem solving in digital space using visual analytics." Computers in Human Behavior 42 (January 2015): 20–35. http://dx.doi.org/10.1016/j.chb.2013.10.061.

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5

Inc, Mustafa, Ali Akgül, and Fazhan Geng. "Reproducing Kernel Hilbert Space Method for Solving Bratu’s Problem." Bulletin of the Malaysian Mathematical Sciences Society 38, no. 1 (2014): 271–87. http://dx.doi.org/10.1007/s40840-014-0018-8.

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6

Glushkov, Vitaly N., and Xavier Assfeld. "Multireference space without first solving the configuration interaction problem." Journal of Computational Chemistry 35, no. 4 (2013): 313–23. http://dx.doi.org/10.1002/jcc.23502.

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7

Balanev, D. J. "Space Modeling of Problem Solving Strategies of “Prisoner's Dilemma”." Sibirskiy Psikhologicheskiy Zhurnal, no. 78 (2021): 99–116. http://dx.doi.org/10.17223/17267080/78/6.

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An iterated version of the game "Prisoner's Dilemma" is used as a model of cooperation largely due to the wide range of strategies that the subjects can use. The problem of the effec-tiveness of strategies for solving the Iterated Prisoner's Dilemma (IPD) is most often considered from the point of view of information models, where strategies do not take into account the relationship that arise when real people play. Some of these strategies are obvious, others depend upon social context. In our paper, we use one of the promising directions in the development of studying IPD strategies – the us

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Margaça, F. M. A., A. N. Falcão, J. F. Salgado, and F. G. Carvalho. "Solving the problem of SANS instrument optimization." Journal of Applied Crystallography 24, no. 6 (1991): 994–98. http://dx.doi.org/10.1107/s0021889891006672.

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The problem of the optimization of small-angle neutron scattering instruments installed at steady neutron sources is discussed. The optimum solution is shown to be that in which full use is made of either the available luminous area of the source or the available hall space and not necessarily that corresponding to the equal-flight-paths design. Design criteria and their implementation are discussed taking into account space constraints on the instrument's layout. It is shown that the performance of currently operating SANS facilities can be substantially improved by following the optimization

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9

Cox, Sherry L. "MATHCOUNTS: The Ultimate in Problem Solving." Arithmetic Teacher 36, no. 2 (1988): 20–26. http://dx.doi.org/10.5951/at.36.2.0020.

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Mathcounts is a mathematics competition for seventh and eighth graders sponsored in part by the National Council of Teachers of Mathematics, the CNA Insurance Compani es, the Nationa l Society of Professional Engineers, the National Aeronautics and Space Administration, the General Motors Foundation, and the Cray Research Foundation. It offers students an opportunity to expand the ir appreciation of mathematics and to improve their problemsolving abi lities.

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WANG, PEI. "PROBLEM SOLVING WITH INSUFFICIENT RESOURCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 05 (2004): 673–700. http://dx.doi.org/10.1142/s0218488504003144.

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A new approach, "controlled concurrency," is introduced for inference control in an adaptive reasoning system working with insufficient knowledge and resources. With this method, a problem-solving process is constructed from atomic steps in run time, according to the system's past experience and the current context. The system carries out many such processes in parallel by distributing its resources among them, and dynamically adjusting the distribution according to feedback. A data structure, "bag," is designed to support this dynamic time-space allocation, and is a kind of probabilistic prio

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11

CHAKRAVARTHY, SHARMA, KAMALAKAR KARLAPALEM, SHAMKANT B. NAVATHE, and ASTERIO TANAKA. "DATABASE SUPPORTED COOPERATIVE PROBLEM SOLVING." International Journal of Cooperative Information Systems 02, no. 03 (1993): 249–87. http://dx.doi.org/10.1142/s0218215793000125.

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Cooperative problem solving can be viewed as a complex activity requiring harmonious and dynamic interaction between active agents and passive agents. This problem is currently being addressed by the research community at various levels of abstraction. Broadly, this paper analyzes the problem of cooperative problem solving from a database perspective and argues that recent advances in database technology facilitate development of a viable solution to the above problem. Specifically, in this paper, we first analyze the problem of cooperative problem solving to identify its underlying key charac

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12

Taddele, Guash Haile, Poom Kumam, Anteneh Getachew Gebrie, and Kanokwan Sitthithakerngkiet. "Half-Space Relaxation Projection Method for Solving Multiple-Set Split Feasibility Problem." Mathematical and Computational Applications 25, no. 3 (2020): 47. http://dx.doi.org/10.3390/mca25030047.

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In this paper, we study an iterative method for solving the multiple-set split feasibility problem: find a point in the intersection of a finite family of closed convex sets in one space such that its image under a linear transformation belongs to the intersection of another finite family of closed convex sets in the image space. In our result, we obtain a strongly convergent algorithm by relaxing the closed convex sets to half-spaces, using the projection onto those half-spaces and by introducing the extended form of selecting step sizes used in a relaxed CQ algorithm for solving the split fe

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13

Stevens, Ron, and Amy Soller. "Machine learning models of problem space navigation: The influence of gender." Computer Science and Information Systems 2, no. 2 (2005): 83–98. http://dx.doi.org/10.2298/csis0502083s.

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We have developed models of how problem spaces are navigated as male and female secondary school, university, and medical students engage in repetitive complex problem solving. The strategies that students used when solving problem-solving simulations were first classified with self-organizing artificial neural networks resulting in problem solving strategy maps. Next, learning trajectories were developed from sequences of performances by Hidden Markov Modeling that stochastically described students' progress in understanding different domains. Across middle school to medical school there were

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14

Wan, Xiao Ping, and Shu Yu Li. "SHP-VI Method of Solving DEC-POMDP Problem." Advanced Materials Research 926-930 (May 2014): 3245–49. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3245.

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DEC-POMDP(Distributed Partially Observable Markov Decision Process) model is a multi-agent model of collaborative decision-making is important, but due to an alarming number of DEC-POMDP problem state space and great strategy solution space, so DEC-POMDP solution of the problem becomes very difficult. The agent from the initial state to the target state during the interaction with the environment, the system's maximum benefit is often only with some small amount of a higher reward states. This article by searching from the initial belief state to the target state to get a shortest Hamiltonian

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15

Lee, Hee Seung, Shawn Betts, and John R. Anderson. "Learning Problem-Solving Rules as Search Through a Hypothesis Space." Cognitive Science 40, no. 5 (2015): 1036–79. http://dx.doi.org/10.1111/cogs.12275.

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16

Gonzales, Alicia C., Stephanie Purington, Julie Robinson, and Martina Nieswandt. "Teacher interactions and effects on group triple problem solving space." International Journal of Science Education 41, no. 13 (2019): 1744–63. http://dx.doi.org/10.1080/09500693.2019.1638982.

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17

Wei, Hu, Alan T. Murray, and Ningchuan Xiao. "Solving the continuous space p-centre problem: planning application issues." IMA Journal of Management Mathematics 17, no. 4 (2006): 413–25. http://dx.doi.org/10.1093/imaman/dpl009.

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18

Inc, Mustafa, and Ali Akguül. "The reproducing kernel Hilbert space method for solving Troesch’s problem." Journal of the Association of Arab Universities for Basic and Applied Sciences 14, no. 1 (2013): 19–27. http://dx.doi.org/10.1016/j.jaubas.2012.11.005.

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19

Bakhtiari, P., S. Abbasbandy, and R. A. Van Gorder. "Solving the Dym initial value problem in reproducing kernel space." Numerical Algorithms 78, no. 2 (2017): 405–21. http://dx.doi.org/10.1007/s11075-017-0381-2.

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20

Barrett, M. J. "Enabling hybrid space: epistemological diversity in socio-ecological problem-solving." Policy Sciences 46, no. 2 (2013): 179–97. http://dx.doi.org/10.1007/s11077-013-9178-x.

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21

Tharpe, Anne Marie, and Gautam Biswas. "Characterization of Problem Solving in Audiology." American Journal of Audiology 6, no. 1 (1997): 31–42. http://dx.doi.org/10.1044/1059-0889.0601.31.

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Characterizing diagnostic problem solving by students, intermediates, and experts may facilitate an understanding of how to help students attain required diagnostic skills. Studies in other specialties characterize experts as spending more time and effort in understanding a problem before they begin to solve it. That is, at the beginning of a problemsolving episode, experts work to limit the problem space. On the other hand, novices often plunge immediately into the solution process. In order to learn more about diagnostic problem-solving in audiology, a computer-based environment, Simon Says

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22

OTSUKA, Kazunori, and Makoto MIYATANI. "Capacity constraints on working memory and the basic problem-solving space: The problem-solving process for the Mastermind game." Japanese Journal of Cognitive Psychology 6, no. 1 (2008): 47–55. http://dx.doi.org/10.5265/jcogpsy.6.47.

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23

Humied, Ismail A. "Solving N-Queens Problem Using Subproblems based on Genetic Algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 3 (2018): 130. http://dx.doi.org/10.11591/ijai.v7.i3.pp130-137.

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<span lang="EN-US">Nowadays, permutation problems with large state spaces and the path to solution is irrelevant such as N-Queens problem has the same general property for many important applications such as integrated-circuit design, factory-floor layout, job-shop scheduling, automatic programming, telecommunications network optimization, vehicle routing, and portfolio management. Therefore, methods which are able to find a solution are very important. Genetic algorithm (GA) is one the most well-known methods for solving N-Queens problem and applicable to a wide range of permutation pro

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24

Chan, R. H. T., P. K. S. Tam, and D. N. K. Leung. "Solving the motion planning problem by using neural networks." Robotica 12, no. 4 (1994): 323–33. http://dx.doi.org/10.1017/s0263574700017343.

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SUMMARYThis paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space whi

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25

Pamungkas, Megita Dwi, and Heny Nugroho. "IMPLEMENTATION OF SPACE GEOMETRY LEARNING USING GEOGEBRA TO IMPROVE PROBLEM SOLVING SKILLS." MaPan 8, no. 2 (2020): 224–35. http://dx.doi.org/10.24252/mapan.2020v8n2a4.

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Prospective mathematics teachers must be equipped with problem solving skills because it has a very important place in the mathematics curriculum. The purpose of this research is to get an overview of improving the problem solving skills of prospective mathematics teachers by implementing GeoGebra on space geometry learning. The research was conducted in Study Program of Mathematics Education, Tidar University in the second semester students. This research uses quasi-experimental method, with nonequivalent control group pretest-posttest design using two groups, namely experimental group consis

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26

Bowden, Edward M. "Accessing relevant information during problem solving: Time constraints on search in the problem space." Memory & Cognition 13, no. 3 (1985): 280–86. http://dx.doi.org/10.3758/bf03197691.

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27

Metz, Kathleen E. "The Development of Children's Problem Solving in a Gears Task: A Problem Space Perspective." Cognitive Science 9, no. 4 (1985): 431–71. http://dx.doi.org/10.1207/s15516709cog0904_4.

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28

Davies, Simon P. "Exploiting constraints in a problem space: Transfer and abstraction in display-based problem solving." Irish Journal of Psychology 14, no. 3 (1993): 463–75. http://dx.doi.org/10.1080/03033910.1993.10557952.

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29

Maharani, Rasendriya Adinda, and Indrie Noor Aini. "Deskripsi Tahapan Problem Solving Siswa Pada Soal Bertipe Pisa Space and Shape Content." Jurnal Cendekia : Jurnal Pendidikan Matematika 5, no. 2 (2021): 1193–200. http://dx.doi.org/10.31004/cendekia.v5i2.608.

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Rendahnya hasil PISA yang berarti rendahnya problem solving siswa terutama pada space and shape content. Terdapat beberapa faktor penyebab salah satunya siswa tidak cukup kompeten dalam menyelesaikan soal-soal bertipe PISA. Sehingga penelitian ini bermaksud untuk mendeskripsikan tahapan problem solving siswa jika diberikan soal bertipe PISA terutama pada space and shape content disalah satu Lembaga Kursus Matematika Jakarta Timur. Waktu penelitian ini dilaksanakan pada bulan April. Jenis penelitian yang diambil yaitu penelitian kualitatif dengan metode analisis deskriptif. Instrumen yang digun

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30

Katz, Irvin R., Michael E. Martinez, Kathleen M. Sheehan, and Kikumi K. Tatsuoka. "Extending the Rule Space Methodology to a Semantically-Rich Domain: Diagnostic Assessment in Architecture." Journal of Educational and Behavioral Statistics 23, no. 3 (1998): 254–78. http://dx.doi.org/10.3102/10769986023003254.

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This paper presents a technique for applying the Rule Space methodology of cognitive diagnosis to assessment in a semantically-rich domain. Previous applications of Rule Space—all in simple, well-structured domains—based diagnosis on examinees’ ability to perform individual problem-solving steps. In a complex domain, however, test items might be so different from one another that the problem-solving steps used for one item are unrelated to the steps used to solve another item. The technique presented herein extends Rule Space’s applicability by basing diagnosis on item characteristics that are

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31

Putra, Mulia, and Rita Novita. "PROFILE OF SECONDARY SCHOOL STUDENTS WITH HIGH MATHEMATICS ABILITY IN SOLVING SHAPE AND SPACE PROBLEM." Journal on Mathematics Education 6, no. 1 (2015): 20–30. http://dx.doi.org/10.22342/jme.6.1.1940.20-30.

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This study aimed to describe the profile of secondary school students with high mathematics ability in solving shape and space problem in PISA (Program for International Student Assessment). It is a descriptive research with a qualitative approach, in which the subjects in this study were students of class VIII SMP N 1 Banda Aceh. The results show that in solving the problem PISA onÂ shape and space, high mathematics ability students were able to identify the problem by making the information known from PISA issues related to the shape and space content.Keyword: Profile, Problem Solving, Task

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32

Kiselev, Dmitrii. "Solving elasticity problem in 2D and 3D space: seismic wave propagation." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1 (May 15, 2015): 45–54. http://dx.doi.org/10.17212/2307-6879-2015-1-45-54.

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33

Chardaire, P., G. P. McKeown, S. A. Verity-Harrison, and S. B. Richardson. "Solving a Time-Space Network Formulation for the Convoy Movement Problem." Operations Research 53, no. 2 (2005): 219–30. http://dx.doi.org/10.1287/opre.1040.0183.

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34

Er, M. C. "A Minimal Space Algorithm for Solving the Towers of Hanoi Problem." Journal of Information and Optimization Sciences 9, no. 2 (1988): 183–91. http://dx.doi.org/10.1080/02522667.1988.10698918.

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35

Kistner, Saskia, Bruce D. Burns, Regina Vollmeyer, and Ulrich Kortenkamp. "An explorative study of search of model space in problem-solving." Journal of Cognitive Psychology 26, no. 7 (2014): 818–29. http://dx.doi.org/10.1080/20445911.2014.960872.

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36

Clode, Linda. "A Crisis in Space — A Futuristic Simulation Using Creative Problem Solving." Gifted Child Today Magazine 15, no. 6 (1992): 52–53. http://dx.doi.org/10.1177/107621759201500616.

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The architect looked solemnly out on the assembled voyagers. “The crack in the ship seems to be stabilized for the moment,” she said. “Our biggest concern is the loss of oxygen tanks which floated out through the crack on impact. Our emergency supplies should last until morning when I hope to have the area redesigned so that we can land to resupply.” The Commander gravely acknowledged her report and called on the rest of the group for comments before moving to the problems of the other members of his team.

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37

Reimann, Peter, Michael Kickmeier-Rust, and Dietrich Albert. "Problem solving learning environments and assessment: A knowledge space theory approach." Computers & Education 64 (May 2013): 183–93. http://dx.doi.org/10.1016/j.compedu.2012.11.024.

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38

Cui, Minggen, and Fazhan Geng. "Solving singular two-point boundary value problem in reproducing kernel space." Journal of Computational and Applied Mathematics 205, no. 1 (2007): 6–15. http://dx.doi.org/10.1016/j.cam.2006.04.037.

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39

Arqub, Omar Abu, Reem Edwan, Mohammed Al-Smadi, and Shaher Momani. "Solving space-fractional Cauchy problem by modified finite-difference discretization scheme." Alexandria Engineering Journal 59, no. 4 (2020): 2409–17. http://dx.doi.org/10.1016/j.aej.2020.03.003.

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40

Fiore, Stephen M., and Jonathan W. Schooler. "Convergent or Divergent Problem Space Search: The Effect of Problem Structure on Group Versus Individual Problem Solving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 45, no. 4 (2001): 483–87. http://dx.doi.org/10.1177/154193120104500446.

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41

Öllinger, Michael, Gary Jones, and Günther Knoblich. "Insight and Search in Katona’s Five-Square Problem." Experimental Psychology 61, no. 4 (2014): 263–72. http://dx.doi.org/10.1027/1618-3169/a000245.

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Insights are often productive outcomes of human thinking. We provide a cognitive model that explains insight problem solving by the interplay of problem space search and representational change, whereby the problem space is constrained or relaxed based on the problem representation. By introducing different experimental conditions that either constrained the initial search space or helped solvers to initiate a representational change, we investigated the interplay of problem space search and representational change in Katona’s five-square problem. Testing 168 participants, we demonstrated that

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42

He, Jianjia, and Fuyuan Xu. "Chaotic-Search-Based Cultural Algorithm for Solving Unconstrained Optimization Problem." Modelling and Simulation in Engineering 2011 (2011): 1–6. http://dx.doi.org/10.1155/2011/239743.

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For premature convergence and instability of cultural algorithm in solving function optimization problem, based on cultural algorithm and chaos search optimization, a chaos cultural algorithm (CCA) is proposed. The algorithm model consists of a chaos-based population space and a knowledge-storing belief space, uses normative knowledge and situational knowledge for chaos search and chaos perturbation, respectively, effectively avoids premature convergence of cultural algorithm, and overcomes chaos search optimization's sensitivity to initial values and poor efficiency. Test results show that th

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43

Hatamlou, Abdolreza. "Solving Travelling Salesman Problem Using Heart Algorithm." International Journal of Applied Evolutionary Computation 8, no. 4 (2017): 32–42. http://dx.doi.org/10.4018/ijaec.2017100103.

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Solving hard problems like the Travelling Salesman Problem (TSP) is a major challenge faced by analysts even though many techniques are available. The main goal of TSP is that a number of cities should be visited by a salesman and return to the starting city along a number of possible shortest paths. TSP is although looking a simple problem, but it is an important problem of the classical optimization problems that are difficult to solve conventionally. It has been proved that solving TSP by the conventional approaches in a reasonable time is not possible. So, the only feasible option left is

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Hinterreiter, Andreas, Christian Steinparz, Moritz SchÖfl, Holger Stitz, and Marc Streit. "Projection Path Explorer: Exploring Visual Patterns in Projected Decision-making Paths." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (2021): 1–29. http://dx.doi.org/10.1145/3387165.

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In problem-solving, a path towards a solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but analysis has focused almost exclusively on the self-similarity of single trajectories. In contrast, we describe patterns emerging from drawing many trajectories—for different initial conditio

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45

Lenin, K., B. Ravindhranath Reddy, and M. Suryakalavathi. "Propagation Algorithm for Solving Optimal Reactive Power Problem." International Journal of Engineering Research in Africa 24 (June 2016): 103–11. http://dx.doi.org/10.4028/www.scientific.net/jera.24.103.

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This paper presents a nature inspired heuristic optimization algorithm based on lightning progression called the propagation algorithm (PA) to solve optimal reactive power problem. It is from the imitated natural phenomenon of lightning and the procedure of step frontrunner propagation using the theory of fast particles. Three particle kinds are established to distinguish the transition particles that produce the first step frontrunner population, the space particles that attempt to turn out to be the frontrunner, and the prime particle that epitomize the particle thrilled from best positioned

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46

Tawfik, Andrew A., Kyung Kim, Maureen Hogan, and Fortunata Msilu. "How Success Versus Failure Cases Support Knowledge Construction in Collaborative Problem-Solving." Journal of Educational Computing Research 57, no. 6 (2018): 1376–99. http://dx.doi.org/10.1177/0735633118799750.

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Theorists suggest that collaboration is a key aspect in online, inquiry-based learning. However, research finds that meaningful interaction is challenging, and learners struggle to sustain interaction. One way to scaffold collaborative problem-solving is through case libraries; however, few studies have explored how the type of experience depicted in a case library (success and failure) catalyzes learning. To address this gap, this study explored how the presence of success versus failure case libraries supports learning in terms of understanding of the problem space, conceptual space, knowled

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47

Al Qasem, R. M., and S. M. Massadeh. "Solving Cell Placement Problem Using Harmony Search Algorithms." Engineering, Technology & Applied Science Research 8, no. 4 (2018): 3172–76. http://dx.doi.org/10.48084/etasr.2113.

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Cell placement is a phase in the chip design process, in which cells are assigned to physical locations. A placement algorithm is a way that satisfies the objectives and minimizes the total area while keeping enough space for routing. Cell placement is an NP-complete problem of very large size. In order to solve this problem, diversified heuristic algorithms are used. In this work, a new algorithm is proposed based on the harmony search algorithm. The harmony search algorithm mimics music improvisation process to find the optimal solution. Cell placement problem has many constraints, so in thi

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48

Sharma, B. "Solving the three-dimensional findpath problem via Liapunov's method." South Pacific Journal of Natural and Applied Sciences 20, no. 1 (2002): 48. http://dx.doi.org/10.1071/sp02010.

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This paper presents a method for solving the findpath problem. Commonly known as the second or direct method of Liapunov, the method is used to solve this geometric problem of finding collision-free trajectories of moving solid objects amongst other fixed and moving solid objects. A Liapunov function is proposed for a n-point dynamical system in three-space. Computer simulations are carried out to show the effectiveness of the proposed Liapunov function-based feedback controllers.

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49

Lenin, K. "WIDE-RANGING VICINITY ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research -GRANTHAALAYAH 5, no. 10 (2017): 361–68. http://dx.doi.org/10.29121/granthaalayah.v5.i10.2017.2314.

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In this paper, Wide-ranging vicinity Algorithm (WVA) is proposed to solve optimal reactive power problem. Wide-ranging vicinity Algorithm equally improves the local & global search. From the global search space a set of arbitrary solutions are primarily generated and then the most excellent solution will give the optimal value. After that, the algorithm will iterate, & there will be two sets of generated solutions in iteration’s, one from the global search space, the other from the set of solutions & it will be produced from the vicinity of the most excellent solution. The proposed

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50

Tautz, R. C., A. Shalchi, and R. Schlickeiser. "Solving the 90° Scattering Problem in Isotropic Turbulence." Astrophysical Journal 685, no. 2 (2008): L165—L168. http://dx.doi.org/10.1086/592498.

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