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Journal articles on the topic 'Learning for planning'

Author: Grafiati

Published: 22 February 2025

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1

Sahraoui, Sofiane. "Learning through Planning." Journal of Organizational and End User Computing 15, no. 2 (2003): 37–53. http://dx.doi.org/10.4018/joeuc.2003040103.

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2

Mally, Kristi. "Planning for Learning." Journal of Physical Education, Recreation & Dance 80, no. 4 (2009): 39–47. http://dx.doi.org/10.1080/07303084.2009.10598309.

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3

Hodgson, David, and Heather Walford. "Planning for learning and learning about planning in social work fieldwork." Journal of Practice Teaching and Learning 7, no. 1 (2006): 50–66. http://dx.doi.org/10.1921/17466105.7.1.50.

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4

Hodgson, David, and Heather Walford. "Planning for learning and learning about planning in social work fieldwork." Journal of Practice Teaching and Learning 7, no. 1 (2012): 50–66. http://dx.doi.org/10.1921/jpts.v7i1.343.

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Fieldwork education is a crucial component of social work education. Many social work students regard their placement experiences as the most profound learning experiences of their studies. The students undertake their field placements in a diverse range of organisational contexts, and in so doing perform a myriad of tasks, adopt a variety of roles, implement a range of practices, and engage with numerous people. Needless to say, social work students have a rich set of learning opportunities within such diversity. An important part of the fieldwork process is the development of learning plans;

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5

Safra, S., and M. Tennenholtz. "On Planning while Learning." Journal of Artificial Intelligence Research 2 (September 1, 1994): 111–29. http://dx.doi.org/10.1613/jair.51.

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This paper introduces a framework for Planning while Learning where an agent is given a goal to achieve in anenvironment whose behavior is only partially known to the agent. We discuss the tractability of various plan-design processes. We show that for a large natural class of Planning while Learning systems, a plan can be presented and verified in a reasonable time. However, coming up algorithmically with a plan, even for simple classes of systems is apparently intractable. We emphasize the role of off-line plan-design processes, andshow that, in most natural cases, the verification (projecti

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Cowley, Jennifer S. Evans, Thomas W. Sanchez, Nader Afzalan, Abel Silva Lizcano, Zachary Kenitzer, and Thomas Evans. "Learning About E-Planning." International Journal of E-Planning Research 3, no. 3 (2014): 53–76. http://dx.doi.org/10.4018/ijepr.2014070104.

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TechniCity (Technology and Cities) offered in May, 2013 was the first city planning Massive Open Online Course (MOOC). More than 21,000 students registered for the course, which was composed of video lectures, projects, assignments, peer evaluation, and on-line discussion over a four week period. This MOOC experimented with using field based learning, combined with extensive student engagement. The objective was to extend the type of learning environment typically found in city planning classes and similar to what is being done in several other disciplines. This article describes the first sta

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7

Schaeffer, Jonathan. "Games: Planning and Learning." ICGA Journal 17, no. 1 (1994): 40–41. http://dx.doi.org/10.3233/icg-1994-17113.

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8

Hufford, Jon R. "Planning for Distance Learning." Journal of Library Administration 32, no. 1-2 (2001): 259–66. http://dx.doi.org/10.1300/j111v32n01_04.

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9

Zorc, Samo. "Learning in Assembly Planning." IFAC Proceedings Volumes 31, no. 7 (1998): 17–22. http://dx.doi.org/10.1016/s1474-6670(17)40250-3.

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10

Zuiderwijk, Dianka C., Riana Steen, and Pedro N. P. Ferreira. "Learning from operational planning." International Journal of Business Continuity and Risk Management 13, no. 2 (2023): 165–87. http://dx.doi.org/10.1504/ijbcrm.2023.131863.

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11

Divjak, Blaženka, Darko Grabar, Barbi Svetec, and Petra Vondra. "Balanced Learning Design Planning." Journal of information and organizational sciences 46, no. 2 (2022): 361–75. http://dx.doi.org/10.31341/jios.46.2.6.

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We present a comprehensive learning design (LD) concept and tool, motivated by theneeds identified by higher education (HE) practitioners. The concept and tool aim atimplementing contemporary research findings and theory to support balanced LDplanning (BDP). The student-centered BDP concept and tool provide innovation to LDplanning by strongly focusing on learning outcomes (LOs) and student workload,aligning study program and course level LOs, ensuring constructive alignment andassessment validity, enhancing LD by using learning analytics, and enabling flexibleuse in different contexts and ped

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Steen, Riana, Pedro N. P. Ferreira, and Dianka C. Zuiderwijk. "Learning from operational planning." International Journal of Business Continuity and Risk Management 13, no. 2 (2023): 165–87. http://dx.doi.org/10.1504/ijbcrm.2023.10057306.

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13

Bullock, Kate, and Felicity Wikeley. "Personal Learning Planning: strategies for pupil learning." FORUM 43, no. 2 (2001): 67. http://dx.doi.org/10.2304/forum.2001.43.2.8.

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14

Yamada, Seiji. "Learning in robotics. Learning for Reactive Planning." Journal of the Robotics Society of Japan 13, no. 1 (1995): 38–43. http://dx.doi.org/10.7210/jrsj.13.38.

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15

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

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16

Quach, Hong-Nam, Hyeonjun Jo, Sungwoong Yeom, and Kyungbaek Kim. "Link Stability aware Reinforcement Learning based Network Path Planning." Korean Institute of Smart Media 11, no. 5 (2022): 82–90. http://dx.doi.org/10.30693/smj.2022.11.5.82.

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Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is

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17

Bullock, Kate, and Felicity Wikeley. "Personal Learning Planning: Can Tutoring Improve Pupils' Learning?" Pastoral Care in Education 21, no. 1 (2003): 18–25. http://dx.doi.org/10.1111/1468-0122.00250.

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18

Irlam, Gordon. "Machine Learning for Retirement Planning." Journal of Retirement 8, no. 1 (2020): 32–39. http://dx.doi.org/10.3905/jor.2020.1.067.

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19

Hu, Yuepeng, Lehan Yang, and Yizhu Lou. "Path Planning with Q-Learning." Journal of Physics: Conference Series 1948, no. 1 (2021): 012038. http://dx.doi.org/10.1088/1742-6596/1948/1/012038.

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20

McLaughlin- Graham, Karen, and Zane L. Berge. "Strategic Planning And Online Learning." i-manager's Journal of Educational Technology 2, no. 3 (2005): 24–29. http://dx.doi.org/10.26634/jet.2.3.882.

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21

Smith, Peter A. C. "Case study: planning as learning." Action Learning: Research and Practice 4, no. 1 (2007): 77–86. http://dx.doi.org/10.1080/14767330701233897.

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22

Jones, Caroline. "Planning and assessing children's learning." Practical Pre-School 2005, no. 51 (2005): 1–2. http://dx.doi.org/10.12968/prps.2005.1.51.39934.

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23

White, Stacey Swearingen, and James M. Mayo. "Learning Expectations in Environmental Planning." Journal of Planning Education and Research 24, no. 1 (2004): 78–88. http://dx.doi.org/10.1177/0739456x04267712.

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24

Thomas, Earl. "Thoughtful Planning Fosters Learning Transfer." Adult Learning 18, no. 3-4 (2007): 4–8. http://dx.doi.org/10.1177/104515950701800301.

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25

Mulvey, John M. "Machine Learning and Financial Planning." IEEE Potentials 36, no. 6 (2017): 8–13. http://dx.doi.org/10.1109/mpot.2017.2737200.

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26

Grodach, Carl. "Video Learning in Community Planning." Journal of Planning Education and Research 40, no. 4 (2018): 482–90. http://dx.doi.org/10.1177/0739456x18789463.

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This article explores the use of video as an experiential learning tool in planning education. We report on the design of a video learning assignment for undergraduate community planning students and the results of a pre- and postsurvey used to gauge the student learning experience. Results show that video-making can be an effective tool to inspire students to make connections between complex urban theory and planning content and their everyday surroundings. This approach may be a useful support for future planners whose roles will involve community engagement and developing scenarios for comm

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27

Tyson, B. Trevor, and Nicholas P. Low. "Experiential Learning in Planning Education." Journal of Planning Education and Research 7, no. 1 (1987): 15–27. http://dx.doi.org/10.1177/0739456x8700700102.

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28

Wood, Jared, and J. Karl Hedrick. "Partition Learning for Multiagent Planning." Journal of Robotics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/590479.

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Automated surveillance of large geographic areas and target tracking by a team of autonomous agents is a topic that has received significant research and development effort. The standard approach is to decompose this problem into two steps. The first step is target track estimation and the second step is path planning by optimizing directly over target track estimation. This standard approach works well in many scenarios. However, an improved approach is needed for the scenario when general, nonparametric estimation is required, and the number of targets is unknown. The focus of this paper is

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29

Bennett, Scott. "Putting Learning into Library Planning." portal: Libraries and the Academy 15, no. 2 (2015): 215–31. http://dx.doi.org/10.1353/pla.2015.0014.

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30

Buşoniu, Lucian, Alexander Daniels, and Robert Babuška. "Online learning for optimistic planning." Engineering Applications of Artificial Intelligence 55 (October 2016): 70–82. http://dx.doi.org/10.1016/j.engappai.2016.05.003.

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31

Villarroel, Armando. "Planning of distance-learning projects." Prospects 18, no. 1 (1988): 55–61. http://dx.doi.org/10.1007/bf02192958.

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32

Lamanna, Leonardo, Luciano Serafini, Mohamadreza Faridghasemnia, et al. "Planning for Learning Object Properties." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12005–13. http://dx.doi.org/10.1609/aaai.v37i10.26416.

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Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper d

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33

Brews, Peter J., and Michelle R. Hunt. "Learning to plan and planning to learn: resolving the planning school/learning school debate." Strategic Management Journal 20, no. 10 (1999): 889–913. http://dx.doi.org/10.1002/(sici)1097-0266(199910)20:10<889::aid-smj60>3.0.co;2-f.

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34

Grooms, Wes, and Emmanuel Frimpong Boamah. "Toward a Political Urban Planning: Learning from Growth Machine and Advocacy Planning to “Plannitize” Urban Politics." Planning Theory 17, no. 2 (2017): 213–33. http://dx.doi.org/10.1177/1473095217690934.

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This article examines Advocacy Planning through the lens of political–economic urban governance theories—primarily Growth Machine. The first part of the article engages Advocacy Planning and Growth Machine in a conceptual dialogue to search for new insights into the causes of, and potential solutions to, planning’s hitherto inability to significantly mitigate urban social inequity and injustice. The analysis corroborates long-standing assertions of planning’s ineffectiveness in redressing inequitable urban planning outcomes as being resultant of the unequal—and dominant—power held by the gover

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35

Luo, Sha, and Lambert Schomaker. "Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning." Robotics and Autonomous Systems 170 (December 2023): 104545. http://dx.doi.org/10.1016/j.robot.2023.104545.

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36

Dong, Diwen. "College English Learning Center Planning Based on Language Learning." Tobacco Regulatory Science 7, no. 5 (2021): 4493–99. http://dx.doi.org/10.18001/trs.7.5.2.15.

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Objectives: Planning for English learning centers for college students can meet the needs of students’ independent learning and achieve the purpose of enhancing students’ comprehensive English practice and application ability. Methods: This study proposed the characteristics and functions of the English learning center, as well as the resources and facilities of the learning center when planning the university English learning center, and explained the construction of the English learning center’s learning materials and the division of functional areas. The influencing factors of the construct

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37

Syafe'i, Isop, and Ai Fitria Ulfah. "IMPLEMENTATION OF BEHAVIORISM LEARNING THEORIES IN ARABIC LEARNING PLANNING." Al Mi'yar: Jurnal Ilmiah Pembelajaran Bahasa Arab dan Kebahasaaraban 3, no. 2 (2020): 197. http://dx.doi.org/10.35931/am.v3i2.298.

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<p>The purpose of this research to know the theory of learning behaviorism through BF Skinner's thinking in planning Arabic learning. This research method is literature study with qualitative approach, data is extracted through literature study and analyzed through content analysis. The primary data source in this study is The Theories of Learning by Ratna Willis Dahar. While secondary data in this study were obtained from literature exploration related to the discussion. The results of the analysis show that the BF Skinner theory can be applied in Arabic learning planning that is the ma

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38

Shah, Naman. "Learning Neuro-Symbolic Abstractions for Robot Planning and Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23417–18. http://dx.doi.org/10.1609/aaai.v38i21.30409.

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Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstrac

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39

Chen, Long, Xuemin Hu, Bo Tang, and Dongpu Cao. "Parallel Motion Planning: Learning a Deep Planning Model against Emergencies." IEEE Intelligent Transportation Systems Magazine 11, no. 1 (2019): 36–41. http://dx.doi.org/10.1109/mits.2018.2884515.

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40

Abdulayeva, A. "THE ROLE OF LESSON PLANNING AND DESIGN IN THE LEARNING PROCESS." Bulletin of Dulaty University 14, no. 2 (2024): 67–74. http://dx.doi.org/10.55956/xhpi2655.

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This paper conducts an analysis of e-library publications focusing on the keyword "lesson planning". Through a comprehensive review of relevant literature, the study identifies key advantages associated with lesson planning and underscores its pivotal role in enhancing the quality of the educational process. The findings not only shed light on the overall impact of lesson planning but also provide valuable insights into the nuances that contribute to its effectiveness. Furthermore, the paper offers practical strategies tailored for future educators to optimize their lesson planning endeavors.

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41

Chaulwar, Amit. "Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning." International Journal of Machine Learning and Computing 11, no. 1 (2021): 1–11. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1007.

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The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally int

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42

Ya-Yu Huang, Ya-Yu Huang, Zi-Wen Li Ya-Yu Huang, Chun-Hao Yang Zi-Wen Li, and Yueh-Min Huang Chun-Hao Yang. "Automatic Path Planning for Spraying Drones Based on Deep Q-Learning." 網際網路技術學刊 24, no. 3 (2023): 565–75. http://dx.doi.org/10.53106/160792642023052403001.

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<p>The reduction of the agricultural workforce due to the rapid development of technology has resulted in labor shortages. Agricultural mechanization, such as drone use for pesticide spraying, can solve this problem. However, the terrain, culture, and operational limitations in mountainous orchards in Taiwan make pesticide spraying challenging. By combining reinforcement learning with deep neural networks, we propose to train drones to avoid obstacles and find optimal paths for pesticide spraying that reduce operational difficulties, pesticide costs, and battery consumption. We experimen

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43

Gafurdjanovna, Babakhodjaeva Lobar. "Technology of Planning of Blended Learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (2021): 83–88. http://dx.doi.org/10.17762/turcomat.v12i4.476.

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This article discusses the technologies of organizing mixed type lessons. In particular, there was a description of the technology of mixed lessons, their types, methods of use. This article also reveals the features, technical and important aspects of the materials used in mixed education that distinguish them from ordinary lessons.

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44

Witkosky, David V., and Paul Sandrock. "Planning Curriculum for Learning World Languages." Die Unterrichtspraxis / Teaching German 36, no. 1 (2003): 100. http://dx.doi.org/10.2307/3531702.

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45

Riley, Jeni, and Anna Craft. "Primary Education: Assessing and Planning Learning." British Journal of Educational Studies 44, no. 4 (1996): 463. http://dx.doi.org/10.2307/3121927.

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46

Hernández-Sellés, Nuria, Mercedes González-Sanmamed, and Pablo-César Muñoz-Carril. "Planning collaborative learning in virtual environments." Comunicar 21, no. 42 (2014): 25–33. http://dx.doi.org/10.3916/c42-2014-02.

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Collaborative learning has a strong presence in technology-supported education and, as a result, practices being developed in the form of Computer Supported Collaborative Learning (CSCL) are more and more common. Planning seems to be one of the critical issues when elaborating CSCL proposals, which necessarily take into account technological resources, methodology and group configuration as a means to boost exchange and learning in the community. The purpose of this study is to analyze the relevance of the CSCL planning phase and weigh up the significance of its key design components as well a

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47

Toyer, Sam, Sylvie Thiébaux, Felipe Trevizan, and Lexing Xie. "ASNets: Deep Learning for Generalised Planning." Journal of Artificial Intelligence Research 68 (May 4, 2020): 1–68. http://dx.doi.org/10.1613/jair.1.11633.

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In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the AS

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48

Domshlak, C., E. Karpas, and S. Markovitch. "Online Speedup Learning for Optimal Planning." Journal of Artificial Intelligence Research 44 (August 21, 2012): 709–55. http://dx.doi.org/10.1613/jair.3676.

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Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible

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49

Junyent, Miquel, Vicenç Gómez, and Anders Jonsson. "Hierarchical Width-Based Planning and Learning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 519–27. http://dx.doi.org/10.1609/icaps.v31i1.15999.

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Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of the size of the state-space, but exponentially in the problem width. In practice, running the algorithm with a width larger than 1 is computationally intractable, prohibiting IW from solving higher width problems. In this paper, we present a hierarchical algorithm that plans at two levels of abstraction. A high-level planner uses abstract features that are incrementally discove

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50

Oxford, Raquel, and Paul Sandrock. "Planning Curriculum for Learning World Languages." Hispania 87, no. 4 (2004): 743. http://dx.doi.org/10.2307/20140901.

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