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Journal articles on the topic 'Planning and learning'

Author: Grafiati

Published: 18 April 2022

Last updated: 18 July 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; these plans guide and direct the students’ roles, tasks and learning, and are often an important framework by which assessment of competency and learning takes place. However, learning plans presuppose a logical and conceptual clarity, which needs to be learned if they are to be functional and effective documents. This then poses many challenges in relation to how students might develop a learning plan for fieldwork. This paper explores some of the problems, and offers practical guidance, for students and fieldwork educators to develop rational learning plans in diverse and complex contexts.

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5

Vali, Mr Shaik Nagur, S. Kelly, G. Sai Suhani, M. Praveen Kumar, and D. Sravan Kumar. "Smart Meal Planning For Fitness Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 12110–17. https://doi.org/10.55248/gengpi.6.0625.2392.

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6

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 (projection) part canbe carried out in an efficient algorithmic manner.

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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 stage of research, describing course structure and providing initial findings on both course and student outcomes. Compared to students enrolling in traditional, for-credit classes, students in this MOOC reported a range of backgrounds, motivations, and expectations. The data collected also provide insights on student course activity including completion. This information obtained from the class can be used to improve future course offerings. This article documents a pedagogical approach that is still very new and lacking a significant base of literature and comparative studies. The article conclude by suggesting a variety of topics for further research.

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8

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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9

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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10

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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11

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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12

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 pedagogical approaches. The ongoing work has been doneaccording to design science methodology, with positive first feedback from HEpractitioners. We identify areas for further research and improvement, including testingthe BDP tool in real-world HE contexts and its integration with learning managementsystems. This could help close the gap between intended (often innovative) LDs andtheir implementation in real teaching and learning environments.

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13

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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14

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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15

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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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 proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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 community change.

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28

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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29

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 to present a new approach that inherently handles the task to search for and track anunknownnumber of targets within alargegeographic area. This approach is designed for the case when the search is performed by a team of autonomous agents and target estimation requires general, nonparametric methods. There are consequently very few assumptions made. The only assumption made is that a time-changing target track estimation is available and shared between the agents. This estimation is allowed to be general and nonparametric. Results are provided that compare the performance of this new approach with the standard approach. From these results it is concluded that this new approach improves search and tracking when the number of targets is unknown and target track estimation is general and nonparametric.

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30

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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31

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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32

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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33

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 describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.

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34

Nurudin, Arif. "Engineering Mechanics Learning Model Planning." Greenation International Journal of Engineering Science 1, no. 2 (2023): 67–75. https://doi.org/10.38035/gijes.v1i1.28.

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Engineering Mechanics is a branch of the branch of mechanics with the stem of physics. The ability of students in the Engineering Mechanics course is low on average, so various learning models are needed to improve students' ability to achieve their competency standards. This research was conducted to make a plan for the learning model of Engineering mechanics at the Faculty of Engineering, University of Muhammadiyah Cirebon, which was based on the results of research in various educational institutions with various learning models, the purpose of this research is so that students can follow the Engineering mechanics course comfortably and enjoyably. The method used in this study is a literature review from various sources. The results showed that planning the learning model of Engineering mechanics needs to involve sharing dimensions of human life, not limited to cognitive, affective, and psychomotor, but also cooperation, collaboration, the friendship between friends, play, and the active role of lecturers to unite in student activities to do assignments both in groups and individually. Several models can be applied together including NHT, HOTS, STAD, Expository, innovative learning, social relations, and pair-check models.

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35

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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36

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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37

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 construction of learning center materials mainly include students’ language level, learning needs, authority and applicability of learning materials. Results: On this basis, taking the English learning center plan of a university library as an example, the functional areas are divided into four functional areas: English listening, speaking, reading and writing. Conclusion: It is hoped that this research will provide some reference and reference for the planning study of university English learning center based on language learning.

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38

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 material being studied is analyzed up to the units organically, the subject matter is used a module system, learning evaluation must be notified to students, if incorrectly corrected and if properly strengthened, more tests emphasized for the sake of diagnostics, in education prioritizing changing the environment to avoid violations so as not to punish, prioritizing the needs that will lead to operant behavior, the behavior desired by educators are rewarded, the desired behavior is analyzed in small ways, increasingly reaching goals, implementing mastery learning is to learn the material thoroughly according to each time because each child has a different rhythm.</p>

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39

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 abstractions to boost robot planning performance and provide strong guarantees of reliability.

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40

Rohit, Ranjan, Bhakta Himadri, Jha Animesh, and Maheshwari Parv. "[Re] Differentiable Spatial Planning using Transformers." ReScience C 8, no. 2 (2022): #34. https://doi.org/10.5281/zenodo.6574693.

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41

Volkova, Elena, and Emily Smith. "INVESTIGATING DATA GENERATION STRATEGIES FOR LEARNING HEURISTIC FUNCTIONS IN CLASSICAL PLANNING." International Journal of Advanced Artificial Intelligence Research 2, no. 04 (2025): 1–7. https://doi.org/10.55640/ijaair-v02i04-01.

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In classical planning, the efficiency and effectiveness of heuristic functions are crucial for guiding search algorithms toward optimal solutions. This study investigates various data generation strategies for training machine learning models to learn heuristic functions in classical planning domains. By comparing approaches such as random sampling, goal-directed sampling, and domain-specific guided data collection, the research evaluates their impact on the accuracy and generalizability of learned heuristics. Experimental results across benchmark planning problems reveal that the choice of data generation strategy significantly influences the performance of the resulting heuristics. The study provides insights into the trade-offs between data diversity, representativeness, and computational efficiency, contributing to the development of more robust learning-based planning systems.

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42

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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43

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. Drawing upon the insights gleaned from the literature, the suggested approaches aim to empower prospective teachers in crafting detailed and innovative lesson plans. Emphasizing the significance of these strategies, the paper demonstrates their potential to facilitate active student engagement, improve learning outcomes, and maintain classroom discipline. In essence, this analysis serves as a valuable contribution to the field of educational research, offering actionable recommendations for both current teaching practices and the preparation of aspiring educators. The synthesis of literature and practical insights creates a resource that can inform pedagogical strategies and foster continuous improvement in lesson planning methodologies.

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44

Ana, Esteso, Peidro David, Mula Josefa, and Díaz-Madroñero Manuel. "Reinforcement learning applied to production planning and control." International Journal of Production Research, no. 2022 (August 6, 2022): 1–18. https://doi.org/10.1080/00207543.2022.2104180.

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The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.

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45

Chen, Dillon Z., Felipe Trevizan, and Sylvie Thiébaux. "Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 68–76. http://dx.doi.org/10.1609/icaps.v34i1.31462.

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Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learning methods which have up to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude faster than the state-of-the-art deep learning for planning models. Our novel approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the hFF heuristic in a fair competition setting. It also outperforms or ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on plan quality. WL-GOOSE is the first learning for planning model which achieves these feats. Furthermore, we study the connections between our novel WL feature generation method, previous theoretically flavoured learning architectures, and Description Logic Features for planning.

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46

Aldahdouh, Alaa A., and António J. Osório. "Planning To Design MOOC? Think First!" Online Journal of Distance Education and e-Learning 4, no. 2 (2016): 47–57. https://doi.org/10.5281/zenodo.48804.

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Over the last years, educators have been forced to rethink about the whole education system. In 2005, Connectivism, a new learning theory, was emerged. Consequently, Massive Open Online Courses (MOOCs) have been presented as an alternative powerful educational system. Money was invested and tens of for-profit and non-profit companies involved in producing MOOC. However, integrating and adopting MOOC in educational institutions worldwide is still questionable. This literature review paper addressed and discussed the issues that higher education institutions should consider before adopting MOOC. The findings showed eight considerable, interrelated and controllable MOOC issues: high dropout rate, accreditation, business model, reputation, pedagogy, research ethics, student assessment and language barrier. Policy makers in higher education institutions should be aware of these issues before including MOOC in their development plans. In addition, the paper presented a number of possible future studies. 

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47

Hogg, Chad, Ugur Kuter, and Hector Munoz-Avila. "Learning Methods to Generate Good Plans: Integrating HTN Learning and Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1530–35. http://dx.doi.org/10.1609/aaai.v24i1.7571.

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We consider how to learn Hierarchical Task Networks (HTNs) for planning problems in which both the quality of solution plans generated by the HTNs and the speed at which those plans are found is important. We describe an integration of HTN Learning with Reinforcement Learning to both learn methods by analyzing semantic annotations on tasks and to produce estimates of the expected values of the learned methods by performing Monte Carlo updates. We performed an experiment in which plan quality was inversely related to plan length. In two planning domains, we evaluated the planning performance of the learned methods in comparison to two state-of-the-art satisficing classical planners, FastForward and SGPlan6, and one optimal planner, HSP*. The results demonstrate that a greedy HTN planner using the learned methods was able to generate higher quality solutions than SGPlan6 in both domains and FastForward in one. Our planner, FastForward, and SGPlan6 ran in similar time, while HSP* was exponentially slower.

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48

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 intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.

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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 experimented with different reward mechanisms, neural network depths, flight direction granularities, and environments to devise a plan suitable for sloping orchards. Reinforcement learning is more effective than traditional algorithms for solving path planning in complex environments.</p> <p>&nbsp;</p>

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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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