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Relevant bibliographies by topics / Bandit learning

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Academic literature on the topic 'Bandit learning'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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Journal articles on the topic "Bandit learning"

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Ciucanu, Radu, Pascal Lafourcade, Gael Marcadet, and Marta Soare. "SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits." Journal of Artificial Intelligence Research 73 (February 23, 2022): 737–65. http://dx.doi.org/10.1613/jair.1.13163.

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The multi-armed bandit is a reinforcement learning model where a learning agent repeatedly chooses an action (pull a bandit arm) and the environment responds with a stochastic outcome (reward) coming from an unknown distribution associated with the chosen arm. Bandits have a wide-range of application such as Web recommendation systems. We address the cumulative reward maximization problem in a secure federated learning setting, where multiple data owners keep their data stored locally and collaborate under the coordination of a central orchestration server. We rely on cryptographic schemes and

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Azizi, Javad, Branislav Kveton, Mohammad Ghavamzadeh, and Sumeet Katariya. "Meta-Learning for Simple Regret Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6709–17. http://dx.doi.org/10.1609/aaai.v37i6.25823.

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We develop a meta-learning framework for simple regret minimization in bandits. In this framework, a learning agent interacts with a sequence of bandit tasks, which are sampled i.i.d. from an unknown prior distribution, and learns its meta-parameters to perform better on future tasks. We propose the first Bayesian and frequentist meta-learning algorithms for this setting. The Bayesian algorithm has access to a prior distribution over the meta-parameters and its meta simple regret over m bandit tasks with horizon n is mere O(m / √n). On the other hand, the meta simple regret of the frequentist

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Sharaf, Amr, and Hal Daumé III. "Meta-Learning Effective Exploration Strategies for Contextual Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9541–48. http://dx.doi.org/10.1609/aaai.v35i11.17149.

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In contextual bandits, an algorithm must choose actions given ob- served contexts, learning from a reward signal that is observed only for the action chosen. This leads to an exploration/exploitation trade-off: the algorithm must balance taking actions it already believes are good with taking new actions to potentially discover better choices. We develop a meta-learning algorithm, Mêlée, that learns an exploration policy based on simulated, synthetic con- textual bandit tasks. Mêlée uses imitation learning against these simulations to train an exploration policy that can be applied to true con

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Charniauski, Uladzimir, and Yao Zheng. "Autoregressive Bandits in Near-Unstable or Unstable Environment." American Journal of Undergraduate Research 21, no. 2 (2024): 15–25. http://dx.doi.org/10.33697/ajur.2024.116.

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AutoRegressive Bandits (ARBs) is a novel model of a sequential decision-making problem as an autoregressive (AR) process. In this online learning setting, the observed reward follows an autoregressive process, whose action parameters are unknown to the agent and create an AR dynamic that depends on actions the agent chooses. This study empirically demonstrates how assigning the extreme values of systemic stability indexes and other reward-governing parameters severely impairs the ARBs learning in the respective environment. We show that this algorithm suffers numerically larger regrets of high

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Zhao, Yunfan, Tonghan Wang, Dheeraj Mysore Nagaraj, Aparna Taneja, and Milind Tambe. "The Bandit Whisperer: Communication Learning for Restless Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23404–13. https://doi.org/10.1609/aaai.v39i22.34508.

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Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics. However, classic RMAB models largely overlook the challenges of (systematic) data errors - a common occurrence in real-world scenarios due to factors like varying data collection protocols and intentional noise for differential privacy. We demonstrate that conventional RL algorithms used to train RMABs can struggle to perform well in such settings. To solve this problem, we propose the first communication learni

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Wan, Zongqi, Zhijie Zhang, Tongyang Li, Jialin Zhang, and Xiaoming Sun. "Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10087–94. http://dx.doi.org/10.1609/aaai.v37i8.26202.

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Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon T suffer from the regret of at least the square root of T. In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with the order of the polylog T regrets, exponentially improving the dependence in terms of T. To the best of our knowledge, this is the first provable quantum speedup for regrets of bandit problems and in general exploitation in reinforcement lea

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Yang, Luting, Jianyi Yang, and Shaolei Ren. "Contextual Bandits with Delayed Feedback and Semi-supervised Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15943–44. http://dx.doi.org/10.1609/aaai.v35i18.17968.

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Contextual multi-armed bandit (MAB) is a classic online learning problem, where a learner/agent selects actions (i.e., arms) given contextual information and discovers optimal actions based on reward feedback. Applications of contextual bandit have been increasingly expanding, including advertisement, personalization, resource allocation in wireless networks, among others. Nonetheless, the reward feedback is delayed in many applications (e.g., a user may only provide service ratings after a period of time), creating challenges for contextual bandits. In this paper, we address delayed feedback

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Zhou, Pengjie, Haoyu Wei, and Huiming Zhang. "Selective Reviews of Bandit Problems in AI via a Statistical View." Mathematics 13, no. 4 (2025): 665. https://doi.org/10.3390/math13040665.

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Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes multi-armed bandit (MAB) and stochastic continuum-armed bandit (SCAB) problems, which model sequential decision-making under uncertainty. This review outlines the foundational models and assumptions of bandit problems, explores non-asymptotic theoretical tools like concentration inequalities and minimax regret bounds, and compares frequentist and Bayesian algorithms for managing exploration–exploita

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Qu, Jiaming. "Survey of dynamic pricing based on Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (2024): 160–65. http://dx.doi.org/10.54254/2755-2721/37/20230497.

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Dynamic pricing seeks to determine the most optimal selling price for a product or service, taking into account factors like limited supply and uncertain demand. This study aims to provide a comprehensive exploration of dynamic pricing using the multi-armed bandit problem framework in various contexts. The investigation highlights the prevalence of Thompson sampling in dynamic pricing scenarios with a Bayesian backdrop, where the seller possesses prior knowledge of demand functions. On the other hand, in non-Bayesian situations, the Upper Confidence Bound (UCB) algorithm family gains traction

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Kapoor, Sayash, Kumar Kshitij Patel, and Purushottam Kar. "Corruption-tolerant bandit learning." Machine Learning 108, no. 4 (2018): 687–715. http://dx.doi.org/10.1007/s10994-018-5758-5.

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Dissertations / Theses on the topic "Bandit learning"

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Liu, Fang. "Efficient Online Learning with Bandit Feedback." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587680990430268.

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Klein, Nicolas. "Learning and Experimentation in Strategic Bandit Problems." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-122728.

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Talebi, Mazraeh Shahi Mohammad Sadegh. "Online Combinatorial Optimization under Bandit Feedback." Licentiate thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181321.

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Multi-Armed Bandits (MAB) constitute the most fundamental model for sequential decision making problems with an exploration vs. exploitation trade-off. In such problems, the decision maker selects an arm in each round and observes a realization of the corresponding unknown reward distribution. Each decision is based on past decisions and observed rewards. The objective is to maximize the expected cumulative reward over some time horizon by balancing exploitation (arms with higher observed rewards should be selectedoften) and exploration (all arms should be explored to learn their average rewar

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Lomax, S. E. "Cost-sensitive decision tree learning using a multi-armed bandit framework." Thesis, University of Salford, 2013. http://usir.salford.ac.uk/29308/.

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Decision tree learning is one of the main methods of learning from data. It has been applied to a variety of different domains over the past three decades. In the real world, accuracy is not enough; there are costs involved, those of obtaining the data and those when classification errors occur. A comprehensive survey of cost-sensitive decision tree learning has identified over 50 algorithms, developing a taxonomy in order to classify the algorithms by the way in which cost has been incorporated, and a recent comparison shows that many cost-sensitive algorithms can process balanced, two class

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Sakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.

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Cette thèse s'intéresse au problème de l'apprentissage à partir d'interactions en utilisant le cadre du bandit contextuel hors ligne. En particulier, nous nous intéressons à deux sujets connexes : (1) l'apprentissage de politiques hors ligne avec des certificats de performance, et (2) l'apprentissage rapide et efficace de politiques, pour le problème de recommandation à grande échelle. Pour (1), nous tirons d'abord parti des résultats du cadre d'optimisation distributionnellement robuste pour construire des bornes asymptotiques, sensibles à la variance, qui permettent l'évaluation des performa

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CELLA, LEONARDO. "EFFICIENCY AND REALISM IN STOCHASTIC BANDITS." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/807862.

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This manuscript is dedicated to the analysis of the application of stochastic bandits to the recommender systems domain. Here a learning agent sequentially recommends one item from a catalog of available alternatives. Consequently, the environment returns a reward that is a noisy observation of the rating associated to the suggested item. The peculiarity of the bandit setting is that no information is given about not recommended products, and the collected rewards are the only information available to the learning agent. By relying on them the learner adapts his strategy towards reaching its l

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Liu, Sige. "Bandit Learning Enabled Task Offloading and Resource Allocation in Mobile Edge Computing." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29719.

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The Internet-of-Things (IoT) is envisioned as a promising paradigm for carrying the interconnections of massive devices through various communications protocols. With the rapid development of fifth-generation (5G), IoT has incentivized a large number of new computation-intensive applications and bridges diverse technologies to provide ubiquitous services with intelligence. However, with billions of devices anticipated to be connected in IoT systems in the coming years, IoT devices face a series of challenges from their inherent features. For instance, the IoT devices are usually densely depl

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Jedor, Matthieu. "Bandit algorithms for recommender system optimization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.

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Dans cette thèse de doctorat, nous étudions l'optimisation des systèmes de recommandation dans le but de fournir des suggestions de produits plus raffinées pour un utilisateur.La tâche est modélisée à l'aide du cadre des bandits multi-bras.Dans une première partie, nous abordons deux problèmes qui se posent fréquemment dans les systèmes de recommandation : le grand nombre d'éléments à traiter et la gestion des contenus sponsorisés.Dans une deuxième partie, nous étudions les performances empiriques des algorithmes de bandit et en particulier comment paramétrer les algorithmes traditionnels pour

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Louëdec, Jonathan. "Stratégies de bandit pour les systèmes de recommandation." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30257/document.

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Les systèmes de recommandation actuels ont besoin de recommander des objets pertinents aux utilisateurs (exploitation), mais pour cela ils doivent pouvoir également obtenir continuellement de nouvelles informations sur les objets et les utilisateurs encore peu connus (exploration). Il s'agit du dilemme exploration/exploitation. Un tel environnement s'inscrit dans le cadre de ce que l'on appelle " apprentissage par renforcement ". Dans la littérature statistique, les stratégies de bandit sont connues pour offrir des solutions à ce dilemme. Les contributions de cette thèse multidisciplinaire ada

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Nakhe, Paresh [Verfasser], Martin [Gutachter] Hoefer, and Georg [Gutachter] Schnitger. "On bandit learning and pricing in markets / Paresh Nakhe ; Gutachter: Martin Hoefer, Georg Schnitger." Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2018. http://d-nb.info/1167856740/34.

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Books on the topic "Bandit learning"

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Garofalo, Robert Joseph. Chorale and Shaker dance by John P. Zdechlik: A teaching-learning unit. Meredith Music Publications, 1999.

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Garofalo, Robert Joseph. Suite française by Darius Milhaud: A teaching-learning unit. Meredith Music Publications, 1998.

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Garofalo, Robert Joseph. On a hymnsong of Philip Bliss by David R. Holsinger: A teaching/learning unit. Meredith Music Publications, 2000.

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James, Patterson. Retour au collège: Le pire endroit du monde! Hachette romans, 2016.

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Christopher, Tebbetts, and Park Laura 1980 ill, eds. Just my rotten luck. Little Brown & Company, 2015.

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Bubeck, Sébastian, and Cesa-Bianchi Nicolò. Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems. Now Publishers, 2012.

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Zhao, Qing, and R. Srikant. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan & Claypool Publishers, 2019.

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Zhao, Qing, and R. Srikant. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan & Claypool Publishers, 2019.

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Zhao, Qing. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Springer International Publishing AG, 2019.

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Zhao, Qing, and R. Srikant. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan & Claypool Publishers, 2019.

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Book chapters on the topic "Bandit learning"

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, et al. "Associative Bandit Problem." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_39.

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Mannor, Shie, Xin Jin, Jiawei Han, et al. "k-Armed Bandit." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_424.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, et al. "Multi-Armed Bandit." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_565.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, et al. "Multi-Armed Bandit Problem." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_566.

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Mannor, Shie. "k-Armed Bandit." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_424.

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Madani, Omid, Daniel J. Lizotte, and Russell Greiner. "The Budgeted Multi-armed Bandit Problem." In Learning Theory. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_46.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, et al. "Bandit Problem with Side Information." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_54.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, et al. "Bandit Problem with Side Observations." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_55.

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Agarwal, Mudit, and Naresh Manwani. "ALBIF: Active Learning with BandIt Feedbacks." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05981-0_28.

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Vermorel, Joannès, and Mehryar Mohri. "Multi-armed Bandit Algorithms and Empirical Evaluation." In Machine Learning: ECML 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_42.

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Conference papers on the topic "Bandit learning"

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Agarwal, Arpit, Rohan Ghuge, and Viswanath Nagarajan. "Semi-Bandit Learning for Monotone Stochastic Optimization*." In 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2024. http://dx.doi.org/10.1109/focs61266.2024.00083.

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Xu, Zhuofan, Benedikt Bollig, Matthias Függer, and Thomas Nowak. "Permutation Equivariant Deep Reinforcement Learning for Multi-Armed Bandit." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00140.

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Li, Miao, Siyi Qiu, Jiong Liu, and Wenping Song. "Content Caching Optimization Based on Improved Bandit Learning Algorithm." In 2024 33rd International Conference on Computer Communications and Networks (ICCCN). IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637635.

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Wu, Xiaoyi, and Bin Li. "Achieving Regular and Fair Learning in Combinatorial Multi-Armed Bandit." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications. IEEE, 2024. http://dx.doi.org/10.1109/infocom52122.2024.10621191.

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Khiabani, Arman Nikraftar, and Benoit Macq. "Deep Reinforcement Learning for the Expert Advice Multi-Armed Bandit." In IEEE EUROCON 2025 - 21st International Conference on Smart Technologies. IEEE, 2025. https://doi.org/10.1109/eurocon64445.2025.11073451.

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Huang, Duo. "The Development and Future Challenges of the Multi-armed Bandit Algorithm." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957859.

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Sushma, M., and K. P. Naveen. "Multi-Armed Bandit Based Learning Algorithms for Offloading in Queueing Systems." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683365.

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Hossain, Abrar, Abdel-Hameed A. Badawy, Mohammad A. Islam, Tapasya Patki, and Kishwar Ahmed. "HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach." In 2024 IEEE 31st International Conference on High Performance Computing, Data, and Analytics (HiPC). IEEE, 2024. https://doi.org/10.1109/hipc62374.2024.00011.

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das Dores, Silvia Cristina Nunes, Carlos Soares, and Duncan Ruiz. "Bandit-Based Automated Machine Learning." In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2018. http://dx.doi.org/10.1109/bracis.2018.00029.

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Deng, Kun, Chris Bourke, Stephen Scott, Julie Sunderman, and Yaling Zheng. "Bandit-Based Algorithms for Budgeted Learning." In 2007 7th IEEE International Conference on Data Mining (ICDM '07). IEEE, 2007. http://dx.doi.org/10.1109/icdm.2007.91.

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Reports on the topic "Bandit learning"

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Shum, Matthew, Yingyao Hu, and Yutaka Kayaba. Nonparametric learning rules from bandit experiments: the eyes have it! Institute for Fiscal Studies, 2010. http://dx.doi.org/10.1920/wp.cem.2010.1510.

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Liu, Haoyang, Keqin Liu, and Qing Zhao. Learning in A Changing World: Non-Bayesian Restless Multi-Armed Bandit. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada554798.

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Berlin, Noémie, Jan Dul, Marco Gazel, Louis Lévy-Garboua, and Todd Lubart. Creative Cognition as a Bandit Problem. CIRANO, 2023. http://dx.doi.org/10.54932/anre7929.

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This paper characterizes creative cognition as a multi-armed bandit problem involving a trade-off between exploration and exploitation in sequential decisions from experience taking place in novel uncertain environments. Creative cognition implements an efficient learning process in this kind of dynamic decision. Special emphasis is put on the optimal sequencing of divergent and convergent behavior by showing that divergence must be inhibited at one point to converge toward creative behavior so that excessive divergence is counterproductive. We test this hypothesis in two behavioral experiment

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García Marrugo, Alexandra I., Katherine Olston, Josh Aarts, Dashiell Moore, and Syed Kaliyadan. SCANA: Supporting students’ academic language development at The University of Sydney. Journal of the Australian and New Zealand Student Services Association, 2023. http://dx.doi.org/10.30688/janzssa.2023-2-01.

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In 2021, the Learning Hub at The University of Sydney launched the Student Communication and Needs Analysis (SCANA). This program of support consists of a screening language task and associated support interventions in first year units of study (UoS). The self-marking online screening tool developed by the Language Testing Research Centre at The University of Melbourne classifies students into three bands, with Band 1 identifying students at risk of academic failure due to insufficient language proficiency. All students in selected UoS are encouraged to take SCANA and offered academic language

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature

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Maloney, Megan, Sarah Becker, Andrew Griffin, Susan Lyon, and Kristofer Lasko. Automated built-up infrastructure land cover extraction using index ensembles with machine learning, automated training data, and red band texture layers. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49370.

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Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to s

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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.

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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original ro

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McElhaney, Kevin W., Kelly Mills, Danae Kamdar, Anthony Baker, and Jeremy Roschelle. A Summary and Synthesis of Initial OpenSciEd Research. Digital Promise, 2023. http://dx.doi.org/10.51388/20.500.12265/171.

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This report summarizes and synthesizes OpenSciEd research published as of August 2022, addressing two questions about OpenSciEd: (1) To what extent do teachers enact OpenSciEd units with integrity to its distinctive principles? and (2) To what extent do OpenSciEd teacher tools and professional learning experiences support teachers to enact OpenSciEd with integrity? This review includes 16 publications (journal articles, peer-reviewed conference proceedings, conference papers, doctoral dissertations, and published reports). Five of the papers focus on the design of OpenSciEd materials and do no

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Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42425.

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Abstract:

Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispec

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI)

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