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Journal articles on the topic 'Learning-based planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

PRAKASH, POONAM. "Critical Learning and Reflective Practice through Studio-based Learning in Planning and Architecture Education." Creative Space 3, no. 1 (July 2, 2015): 41–54. http://dx.doi.org/10.15415/cs.2015.31004.

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2

Sha’ari, Syireen Rose. "Ms Problem Based Learning in Media Planning." International Journal of Learning: Annual Review 15, no. 3 (2008): 279–88. http://dx.doi.org/10.18848/1447-9494/cgp/v15i03/45690.

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3

Park, S. C., M. T. Gervasio, M. J. Shaw, and G. F. DeJong. "Explanation-based learning for intelligent process planning." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 6 (1993): 1597–616. http://dx.doi.org/10.1109/21.257757.

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4

Collins, Gregg, Lawrence Birnbaum, Bruce Krulwich, and Michael Freed. "Model-based integration of planning and learning." ACM SIGART Bulletin 2, no. 4 (July 1991): 56–60. http://dx.doi.org/10.1145/122344.122354.

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5

Tianfield, H. "Robot action planning via explanation-based learning." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 2 (March 2000): 216–22. http://dx.doi.org/10.1109/3468.833104.

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6

Wang, Jiankun, Wenzheng Chi, Chenming Li, Chaoqun Wang, and Max Q. H. Meng. "Neural RRT*: Learning-Based Optimal Path Planning." IEEE Transactions on Automation Science and Engineering 17, no. 4 (October 2020): 1748–58. http://dx.doi.org/10.1109/tase.2020.2976560.

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7

Shepherd, Anne, and Bryna Cosgrif. "Problem-Based Learning: A Bridge between Planning Education and Planning Practice." Journal of Planning Education and Research 17, no. 4 (June 1998): 348–57. http://dx.doi.org/10.1177/0739456x9801700409.

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8

Whitley, Heather P., Edward Bell, Marty Eng, David G. Fuentes, Kristen L. Helms, Erik D. Maki, and Deepti Vyas. "Practical Team-Based Learning from Planning to Implementation." American Journal of Pharmaceutical Education 79, no. 10 (December 2015): 149. http://dx.doi.org/10.5688/ajpe7910149.

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9

Hwang, Kao-Shing, Wei-Cheng Jiang, and Yu-Jen Chen. "Pheromone-Based Planning Strategies in Dyna-Q Learning." IEEE Transactions on Industrial Informatics 13, no. 2 (April 2017): 424–35. http://dx.doi.org/10.1109/tii.2016.2602180.

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10

Grasas, Alex, and Helena Ramalhinho. "Teaching distribution planning: a problem-based learning approach." International Journal of Logistics Management 27, no. 2 (August 8, 2016): 377–94. http://dx.doi.org/10.1108/ijlm-05-2014-0075.

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Purpose – The purpose of this paper is to present a problem-based learning (PBL) activity that uses a decision support system (DSS) to teach one of the most fundamental topics in distribution planning: vehicle routing. Design/methodology/approach – The authors describe their teaching experience in a logistics and supply chain management (LSCM) course. In the PBL activity proposed, students need to solve a typical vehicle routing case with no previous theoretical background taught. The paper is written as a teaching guide for other instructors, detailing how the activity may be carried out in class. Findings – The PBL activity involved students from the very beginning, challenging them to solve a rather complicated problem. Its acceptance was very positive according to the student feedback survey conducted after the activity. Only when struggling with the difficulties of the case proposed, did students really appreciate the potential value of a DSS for making better decisions. Moreover, this activity raised concerns about how DSSs must be adapted for implementation in every business scenario. Originality/value – Teaching logistics management goes beyond lecturing on elemental concepts and tools; it is also about applying this knowledge to manage things. Although several PBL initiatives have been reported to be successful in the field of LSCM, this one incorporates a web-based DSS. The main issue in PBL activities is finding authentic and representative problems to develop transferable skills, and currently most logistics problems are solved using DSS.

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11

Boyer, Robert H. W. "Team-Based Learning in the Urban Planning Classroom." Journal of Planning Education and Research 40, no. 4 (April 12, 2018): 460–71. http://dx.doi.org/10.1177/0739456x18769145.

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Team-based learning (TBL) is a pedagogical technique that structures most lessons of a semester around work in permanent teams of five to six students. Drawing from the author’s experience using TBL plus a survey administered to students in an introductory undergraduate urban planning class, this paper illustrates how TBL enacts a nonhierarchical model of knowledge production, creates natural opportunities to practice highly valued skills in the planning profession, and can be implemented in almost any classroom space during typical classroom hours, offering an alternative to the studio or practicum course that is accessible to more courses more often.

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12

Gan, Chenfeng, Wei Liu, Ning Wang, and Xingyu Ye. "Heterogeneous Agent Cooperative Planning Based on Q-Learning." International Journal of Computer Theory and Engineering 13, no. 1 (2021): 17–23. http://dx.doi.org/10.7763/ijcte.2021.v13.1284.

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13

Ikkai, Yoshitomo, Takenao Ohkawa, and Norihisa Komoda. "Recursive learning method for knowledge-based planning system." Journal of Intelligent Manufacturing 7, no. 5 (October 1996): 405–10. http://dx.doi.org/10.1007/bf00123918.

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14

Lu, Qingkai, and Tucker Hermans. "Modeling Grasp Type Improves Learning-Based Grasp Planning." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 784–91. http://dx.doi.org/10.1109/lra.2019.2893410.

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15

Wang, Xiaoqi, Lina Jin, and Haiping Wei. "The Shortest Path Planning Based on Reinforcement Learning." Journal of Physics: Conference Series 1584 (July 2020): 012006. http://dx.doi.org/10.1088/1742-6596/1584/1/012006.

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16

Zuo, Yuan, Yulei Wu, Geyong Min, and Laizhong Cui. "Learning-based network path planning for traffic engineering." Future Generation Computer Systems 92 (March 2019): 59–67. http://dx.doi.org/10.1016/j.future.2018.09.043.

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17

Tsai, Chia-Wen, Pei-Di Shen, and Tsang-Hsiung Lee. "The Effects of Combined Training of Web-Based Problem-Based Learning and Self-Regulated Learning." International Journal of Web-Based Learning and Teaching Technologies 6, no. 2 (April 2011): 40–50. http://dx.doi.org/10.4018/jwltt.2011040103.

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This study explored, via quasi-experiments, the effects of the combined training in web-based problem-based learning (PBL) and self-regulated learning (SRL) on low achieving students’ skill development. Two classes of 76 undergraduates in a one-semester course titled ‘Web Page Programming and Website Planning’ were chosen for this study. Results were generally positive, showing enhanced skills of website planning and higher levels of involvement. This study provided an illustration of a promising course design and its associated implementations in the specific context of low achieving students, for which there is lack of research in the current literature.

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18

Shou, Zhaoyu, Xianying Lu, Zhengzheng Wu, Hua Yuan, Huibing Zhang, and Junli Lai. "On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior." IEEE Access 8 (2020): 119863–79. http://dx.doi.org/10.1109/access.2020.3005793.

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19

Cazorla-Montero, de los Ríos-Carmenado, and Pasten. "Sustainable Development Planning: Master’s Based on a Project-Based Learning Approach." Sustainability 11, no. 22 (November 13, 2019): 6384. http://dx.doi.org/10.3390/su11226384.

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The educational subject of Sustainable Development Planning in Europe is evolving due to the implementation of the Bologna Agreement across the European Higher Education Area (EHEA). This paper analyses a project-based learning strategy for training Sustainable Development Planning in postgraduate programs, in Spain (Universidad Politécnica de Madrid, UPM). This project-based learning strategy is applied to an International Postgraduate Program for Sustainable Rural Development—Erasmus Mundus, Master’s of Science—with the participation of five European Union universities that formed the Agris Mundus Alliance for Sustainable Development. Using a mixed methods approach, the research examined the program’s implementation through student and staff perceptions, from the technical, behavioral and contextual project management skills. The paper argues that the “Practical Learning platforms” used in the Master’s demonstrate the correct approach of the learning strategy based on teaching–research linked to the professional sphere. The findings that were identified can be categorized as follows: (1) Perspective: holistic thinking and intellectual coherence, defining the contextual skills that must be navigated within and across the broader environment, (2) Practice: experiential learning by reconnecting to real-life situations, and (3) People: Personal and interpersonal skills required to succeed in sustainable projects, programs and portfolios. Reflections on the experience and main success factors in the learning strategy are discussed.

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20

Lea, Anne. "menu planning in community-based learning disability inpatient units." Learning Disability Practice 8, no. 5 (June 2005): 33–37. http://dx.doi.org/10.7748/ldp2005.06.8.5.33.c1629.

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21

Riyanti, Menul Teguh. "Development of Learning Devices Commercial Graphic Based Planning Project." International Journal of Education, Training and Learning 2, no. 1 (2018): 1–6. http://dx.doi.org/10.33094/6.2017.2018.21.1.6.

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22

Carnevale, Claudio, Giovanna Finzi, Anna Pederzoli, Enrico Turrini, and Marialuisa Volta. "Lazy Learning based surrogate models for air quality planning." Environmental Modelling & Software 83 (September 2016): 47–57. http://dx.doi.org/10.1016/j.envsoft.2016.04.022.

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23

Mason, David H. "Scenario‐based planning: Decision model for the learning organization." Planning Review 22, no. 2 (February 1994): 6–11. http://dx.doi.org/10.1108/eb054457.

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24

Moll, Mark, Janice Bordeaux, and Lydia E. Kavraki. "Software for project-based learning of robot motion planning." Computer Science Education 23, no. 4 (December 2013): 332–48. http://dx.doi.org/10.1080/08993408.2013.847167.

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25

KAWATA, Seiichi, Toshiyuki TANIMURA, and Toshiki Oguchi. "Reinforcement Learning based Path-Planning of the Mobile Robot." Proceedings of the JSME annual meeting 2003.5 (2003): 239–40. http://dx.doi.org/10.1299/jsmemecjo.2003.5.0_239.

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26

Zhang, Jiren, Hui Chen, Shaoyu Song, and Fengwei Hu. "Reinforcement Learning-Based Motion Planning for Automatic Parking System." IEEE Access 8 (2020): 154485–501. http://dx.doi.org/10.1109/access.2020.3017770.

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27

庄, 夏. "Dynamic Planning Method Based on Time Delayed Q-Learning." Computer Science and Application 07, no. 07 (2017): 671–77. http://dx.doi.org/10.12677/csa.2017.77078.

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28

Lv, Liangheng, Sunjie Zhang, Derui Ding, and Yongxiong Wang. "Path Planning via an Improved DQN-Based Learning Policy." IEEE Access 7 (2019): 67319–30. http://dx.doi.org/10.1109/access.2019.2918703.

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29

Freestone, Robert, Susan Thompson, and Peter Williams. "Student Experiences of Work-Based Learning in Planning Education." Journal of Planning Education and Research 26, no. 2 (December 2006): 237–49. http://dx.doi.org/10.1177/0739456x06295027.

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30

Bhattacharyya, Biplab, and Rohit Babu. "Teaching Learning Based Optimization algorithm for reactive power planning." International Journal of Electrical Power & Energy Systems 81 (October 2016): 248–53. http://dx.doi.org/10.1016/j.ijepes.2016.02.042.

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31

Chen, Tsung-Yi, Hui-Chuan Chu, Yuh-Min Chen, and Kuan-Chun Su. "Ontology-based Adaptive Dynamic e-Learning Map Planning Method for Conceptual Knowledge Learning." International Journal of Web-Based Learning and Teaching Technologies 11, no. 1 (January 2016): 1–20. http://dx.doi.org/10.4018/ijwltt.2016010101.

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E-learning improves the shareability and reusability of knowledge, and surpasses the constraints of time and space to achieve remote asynchronous learning. Since the depth of learning content often varies, it is thus often difficult to adjust materials based on the individual levels of learners. Therefore, this study develops an ontology-based adaptive dynamic knowledge concept e-learning mechanism that generates learning maps based on learner characteristics and guides learners effectively. To achieve this goal, this study proposes an adaptive dynamic concept e-learning navigation procedure, designs learning models based on the adaptive learning needs of learners, and develops knowledge map model and learning map model. Finally, this study designs adaptive dynamic concept learning map-planning algorithms based on the particle swarm optimization (PSO) algorithm. The learning maps generated by these algorithms meet the dynamic needs of learners by continually adjusting and modifying the learning map throughout the learning process. Adapting the adaptive learning content according to the dynamic needs of learners allows learners to receive more instruction in a limited period.

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32

Garnett, Jonathan. "Work-based learning." Higher Education, Skills and Work-Based Learning 6, no. 3 (August 8, 2016): 305–14. http://dx.doi.org/10.1108/heswbl-04-2016-0023.

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Purpose – The purpose of this paper is to show how transdisciplinarity is woven into the key curriculum components of individually negotiated work-based learning (WBL) programmes and to focus upon the performative value of knowledge in the work context. Design/methodology/approach – This paper draws upon WBL academic literature and the authors 22 years operational experience of WBL. Findings – The paper suggests that while university-level WBL can enhance the performance of organizations and individuals it is also inherently challenging and challenged by the hegemony of subject disciplines and disciplinary-based university structures. WBL is concerned with knowledge which is often unsystematic, socially constructed and is action focused in order to achieve outcomes of significance to work. This contests the supremacy of the role of the university in curriculum design, delivery and validation of knowledge and means that work-based knowledge is often seen as transdisciplinary rather than conforming to traditional subject disciplines (Boud and Solomon, 2001). Research limitations/implications – Central to the distinctive nature of university WBL programmes is the role of the external organization as a partner with the university and the individual learner in the planning of learning activities which are intended to have significance for the workplace. For individual knowledge to become organizational knowledge, and thus fully contribute to the intellectual capital of the organization, it must be shared and accepted by others. It follows that a key concern for organizations must be the facilitation of the recognition of knowledge and this goes beyond using a transdisciplinary lens when guiding and assessing the work of individual higher education students. Practical implications – The paper has practical implications for the design and facilitation of WBL programmes at higher education level. Originality/value – Provides an informed and sustained examination of the concept of WBL and knowledge.

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33

CHEN, Yang, Daohui ZHANG, Xingang ZHAO, and Jianda HAN. "UAV 3D Path Planning Based on IHDR Autonomous-Learning-Framework." Robot 34, no. 5 (2012): 513. http://dx.doi.org/10.3724/sp.j.1218.2012.00513.

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34

Kashihara, Akihiro, Ryoichi Suzuki, Shinobu Hasegawa, and Jun'ichi Toyoda. "A Learner-Centered Navigation Planning with Web-based Learning Resources." Transactions of the Japanese Society for Artificial Intelligence 17, no. 4 (2002): 510–20. http://dx.doi.org/10.1527/tjsai.17.510.

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35

Ogay, Dmitriy, and Eun-Gyung Kim. "Heuristics for Motion Planning Based on Learning in Similar Environments." Journal of information and communication convergence engineering 12, no. 2 (June 30, 2014): 116–21. http://dx.doi.org/10.6109/jicce.2014.12.2.116.

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36

KATO, Haruyasu, Hiroki INOUE, Motoki TAKAGI, Yoshiyuki TAKAHASHI, and Takashi KOMEDA. "1P1-A04 Acquisition of Grasp Planning Based on Q-learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2008 (2008): _1P1—A04_1—_1P1—A04_3. http://dx.doi.org/10.1299/jsmermd.2008._1p1-a04_1.

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37

Das, Nikhil, and Michael Yip. "Learning-Based Proxy Collision Detection for Robot Motion Planning Applications." IEEE Transactions on Robotics 36, no. 4 (August 2020): 1096–114. http://dx.doi.org/10.1109/tro.2020.2974094.

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38

Cho, Yong Hyeon, and Chan Gook Park. "A Reinforcement Learning-Based Path Planning Considering Degree of Observability." Proceedings of International Conference on Artificial Life and Robotics 25 (January 13, 2020): 502–5. http://dx.doi.org/10.5954/icarob.2020.gs1-5.

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39

Yang, X. Z., Z. X. Cui, and X. Y. Qiu. "Flight Path Planning Surrogate Model Based on Stacking Ensemble Learning." IOP Conference Series: Materials Science and Engineering 751 (February 7, 2020): 012038. http://dx.doi.org/10.1088/1757-899x/751/1/012038.

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40

Hardie, Mary. "An Inquiry-Based Learning Approach to Teaching about Planning Regulations." Transactions 6, no. 2 (September 2009): 5–18. http://dx.doi.org/10.11120/tran.2009.06020005.

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41

Foley, Richard P., Janina Levy, Hollis J. Russinof, and Maurice R. Lemon. "Planning and implementing a problem‐based learning rotation for residents." Teaching and Learning in Medicine 5, no. 2 (January 1993): 102–6. http://dx.doi.org/10.1080/10401339309539600.

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42

Chen, Chih-Ming. "Ontology-based concept map for planning a personalised learning path." British Journal of Educational Technology 40, no. 6 (November 2009): 1028–58. http://dx.doi.org/10.1111/j.1467-8535.2008.00892.x.

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43

Chen, Chunlin, Daoyi Dong, Ruixing Long, and Bo Qi. "Sampling-based learning control of quantum systems via path planning." IET Control Theory & Applications 8, no. 15 (October 16, 2014): 1513–22. http://dx.doi.org/10.1049/iet-cta.2014.0320.

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44

Reveliotis, Spyros A. "Uncertainty management in optimal disassembly planning through learning-based strategies." IIE Transactions 39, no. 6 (March 22, 2007): 645–58. http://dx.doi.org/10.1080/07408170600897536.

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45

Peng, Jiansheng. "Mobile Robot Path Planning Based on Improved Q Learning Algorithm." International Journal of Multimedia and Ubiquitous Engineering 10, no. 7 (July 31, 2015): 285–94. http://dx.doi.org/10.14257/ijmue.2015.10.7.30.

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46

Xu, Hongwei, Ning Wang, Hong Zhao, and Zhongjiu Zheng. "Deep reinforcement learning-based path planning of underactuated surface vessels." Cyber-Physical Systems 5, no. 1 (November 14, 2018): 1–17. http://dx.doi.org/10.1080/23335777.2018.1540018.

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47

Zhang, Lin, Yingjie Zhang, and Yangfan Li. "Path planning for indoor Mobile robot based on deep learning." Optik 219 (October 2020): 165096. http://dx.doi.org/10.1016/j.ijleo.2020.165096.

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48

Cervera, Enrique, Angel P. Del Pobil, Edward Marta, and Miguel A. Serna. "Perception-based learning for motion in contact in task planning." Journal of Intelligent and Robotic Systems 17, no. 3 (November 1996): 283–308. http://dx.doi.org/10.1007/bf00339665.

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49

Kang, Yuncheol, Seokgi Lee, and Byung Do Chung. "Learning-based logistics planning and scheduling for crowdsourced parcel delivery." Computers & Industrial Engineering 132 (June 2019): 271–79. http://dx.doi.org/10.1016/j.cie.2019.04.044.

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50

Bejjani, Wissam, Matteo Leonetti, and Mehmet R. Dogar. "Learning image-based Receding Horizon Planning for manipulation in clutter." Robotics and Autonomous Systems 138 (April 2021): 103730. http://dx.doi.org/10.1016/j.robot.2021.103730.

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