Relevant bibliographies by topics / Autonomous AI

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Autonomous AI'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Autonomous AI"

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Totschnig, Wolfhart. "Fully Autonomous AI." Science and Engineering Ethics 26, no. 5 (2020): 2473–85. http://dx.doi.org/10.1007/s11948-020-00243-z.

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Cao, Longbing. "AutoAI: Autonomous AI." IEEE Intelligent Systems 37, no. 5 (2022): 3–5. http://dx.doi.org/10.1109/mis.2022.3209046.

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Echenim, Jennifer Ifeoma. "Integration of Artificial Intelligence and Blockchain for Intelligent Autonomous Systems." International Journal of Future Engineering Innovations 2, no. 3 (2025): 31–37. https://doi.org/10.54660/ijfei.2025.2.3.31-37.

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The integration of Artificial Intelligence (AI) and Blockchain offers a transformative approach to the development of Intelligent Autonomous Systems (IAS). Autonomous systems, which include self-driving cars, drones, and robots, require advanced decision-making, real-time data processing, and secure communication. AI provides the intelligence necessary for these systems to operate autonomously, while Blockchain introduces decentralized control, transparency, and enhanced security. This paper explores the potential of combining AI and Blockchain technologies to create more secure, transparent,
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Kumar, Apurva. "Building Autonomous AI Agents based AI Infrastructure." International Journal of Computer Trends and Technology 72, no. 11 (2024): 116–25. https://doi.org/10.14445/22312803/ijctt-v72i11p112.

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Shinde, Dr Pravin, Himali Paradkar, Poojan Vig, Sanchay Thalnerkar, and Vinay Jain. "HELIX: Autonomous AI Agent." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4647–54. http://dx.doi.org/10.22214/ijraset.2024.61080.

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Abstract: Artificial Intelligence has transformed the way we interact with technology, introducing us to agents that can think and make choices like humans. At the heart of this evolution is our project, 'HELIX'. Through 'HELIX', we've developed AI agents specialized in a variety of tasks: from digging deep into the web for research, streamlining email communication through automation, efficiently sending out emails in bulk, to strategically identifying and generating potential business leads. By weaving together cutting-edge machine learning algorithms and advanced language models, our system
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Sai Vyshnavi, Koyya Doondy. "Integration of Blockchain, Internet of Things and AI." International Journal of Research in Science and Technology 12, no. 04 (2022): 31–36. http://dx.doi.org/10.37648/ijrst.v12i04.006.

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The next phase of digital transformation will be propelled by technologies like blockchain, the Internet of Things (IoT), and artificial intelligence (AI). In this paper, we suggest that the convergence of these technologies will make possible novel forms of enterprise. Future autonomous agents will function as autonomous profit centers that have a digital twin leveraging IoT, send and receive money leveraging blockchain technology, and autonomously make decisions as independent economic agents utilizing artificial intelligence and data analytics. Further, we suggest that this convergence will
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Naz, Neelma, Muhammad Khurram Ehsan, Muhammad Rizwan Amirzada, Md Yeakub Ali, and Muhammad Aasim Qureshi. "Intelligence of Autonomous Vehicles: A Concise Revisit." Journal of Sensors 2022 (April 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/2690164.

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Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driv
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Dinesh, Thangaraju. "Autonomous Data Engineering." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 2 (2024): 1–12. https://doi.org/10.5281/zenodo.14763610.

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The increasing complexity of modern data systems has positioned Artificial Intelligence (AI) as a transformative force in data engineering. AI-powered tools and frameworks are streamlining data pipeline orchestration, schema creation, and quality assurance, enabling enterprises to enhance the productivity of data engineers, increase operational speed and agility, and reduce costs. However, the adoption of AI in data engineering also introduces risks related to data security, bias, and compliance that require careful management. This paper explores how AI is reshaping data engineering, focusing
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Jackson, Andrew Srivathsan. "Compliance to Autonomous Intentions." Compliance to Autonomous Intentions 10, no. 3 (2025): 931–33. https://doi.org/10.38124/ijisrt/25mar1042.

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Artificial Intelligence (AI) is evolving beyond just following instructions. It’s starting to make decisions in ways that resemble human-like intentions. This paper explores the point where AI stops simply following rules and starts acting on its own, reflecting human intelligence in both predictable and unexpected ways. It also looks at how AI can withhold knowledge, form patterns of behavior, and even develop a subconscious-like intelligence. Using real-world examples, we analyze cases where AI has surprised its creators, raising questions about control, governance, and ethics. This pa
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Vikram, Ajit, Ken Brudnak, Arwa Zahid, Moonsub Shim, and Paul J. A. Kenis. "Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology." Nanoscale 13, no. 40 (2021): 17028–39. http://dx.doi.org/10.1039/d1nr05497j.

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An AI-assisted autonomous reactor platform enables accelerated synthesis screening of colloidal nanocrystals. The AI-assisted platform autonomously learns to accurately predict the synthesis outcomes across the entire synthesis parameter space.
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Dissertations / Theses on the topic "Autonomous AI"

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Hajj, hassan Houssam. "Enabling autonomous IoT systems : A middleware-based hybrid AI approach to self-adaptation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS029.

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La prolifération de l'Internet des objets (IoT) a transformé les espaces traditionnels en environnements plus intelligents et interconnectés. Cela a donné lieu à des systèmes IoT sophistiqués, généralement composés de dispositifs qui détectent des phénomènes physiques et génèrent des données pouvant être traitées par des nœuds de calcul avant d'être consommées par des applications. Ces applications définissent généralement des exigences spécifiques de qualité de service (QoS) qui doivent être respectées (par exemple, disponibilité, précision, latence). À cette fin, les systèmes IoT sont souven
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Smakaj, Paola. "Applicazione di strumenti e metodi dell'Informatica Forense ai dati contenuti negli autoveicoli." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23045/.

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La tesi si colloca nel perimetro dell'Informatica Forense, o Digital Forensics, la scienza forense che «studia l'insieme delle attività che sono rivolte all'analisi e alla soluzione dei casi legati alla criminalità informatica, comprendendo tra questi i crimini realizzati con l'uso di un computer, diretti a un computer o in cui il computer può comunque rappresentare una fonte di prova». Gli oggetti e i dispositivi che utilizziamo conservano tracce che, se estratte e analizzate da un esperto informatico forense, possono aiutare nell'accertamento di un fatto giuridicamente rilevante. Tuttavia, c
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Aslansefat, K., Sohag Kabir, Amr R. A. Abdullatif, Vinod Vasudevan, and Y. Papadopoulos. "Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems." IEEE, 2021. http://hdl.handle.net/10454/18591.

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Yes<br>This article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-the loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software. The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly. As autonomous vehicles operate in dynamic environments, the risk that they can face an unknown observation is relatively high due to insufficient training data, distributional shi
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Strineholm, Philippe. "Exploring Human-Robot Interaction Through Explainable AI Poetry Generation." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54606.

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As the field of Artificial Intelligence continues to evolve into a tool of societal impact, a need of breaking its initial boundaries as a computer science discipline arises to also include different humanistic fields. The work presented in this thesis revolves around the role that explainable artificial intelligence has in human-robot interaction through the study of poetry generators. To better understand the scope of the project, a poetry generators study presents the steps involved in the development process and the evaluation methods. In the algorithmic development of poetry generators, t
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Stroozas, B. A., D. Biroscak, M. Eckert, et al. "The NASA EUVE Satellite in Transition: From Staffed to Autonomous Science Payload Operations." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611421.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>The science payload for NASA's Extreme Ultraviolet Explorer (EUVE) satellite is controlled from the EUVE Science Operations Center (ESOC) at the Center for EUV Astrophysics (CEA), University of California, Berkeley (UCB). The ESOC is in the process of a transition from a single staffed shift to an autonomous, zero-shift, "lights out" science payload operations scenario (a.k.a., 1:0). The purpose of the 1:0 transition is to automate all of the remainin
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Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an inte
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Habib, Yassine. "Monocular SLAM densification for 3D mapping and autonomous drone navigation." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0390.

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Les drones aériens sont essentiels dans les missions de recherche et de sauvetage car ils permettent une reconnaissance rapide de la zone de la mission, tel qu’un bâtiment effondré. La cartographie 3D dense et métrique en temps réel est cruciale pour capturer la structure de l’environnement et permettre une navigation autonome. L’approche privilégiée pour cette tâche consiste à utiliser du SLAM (Simultaneous Localization and Mapping) à partir d’une caméra monoculaire synchronisée avec une centrale inertielle (IMU). Les algorithmes à l’état de l’art maximisent l’efficacité en triangulant un nom
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Jönsson, Jonatan, and Felix Stenbäck. "Monte-Carlo Tree Search in Continuous Action Spaces for Autonomous Racing : F1-tenth." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42442.

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Autonomous cars involve problems with control and planning. In thispaper, we implement and evaluate an autonomous agent based ona Monte-Carlo Tree Search in continuous action space. To facilitatethe algorithm, we extend an existing simulation framework and usea GPU for faster calculations. We compare three action generatorsand two rewards functions. The results show that MCTS convergesto an effective driving agent in static environments. However, it onlysucceeds at driving slow speeds in real-time. We discuss the problemsthat arise in dynamic and static environments and look to future workin i
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Becze, Joseph. "Volvo VISE." Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171730.

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Every year over 1.35 Million lives are lost to road accidents. Trucks are probably the most dangerous vehicles on public roads due to their size and mass. 15% of all accidents involve trucks and the majority of victims are car occupants. Most of these accidents happen outside of urban areas at high speeds. In the dawn of autonomous drive and electromobility the trucking industry has the opportunity to reinvent their products. Volvo Trucks is a company that has Safety in its DNA. Future Volvo trucks could be tailored around Safety to save lives and bring justice to this core value. Autonomy has
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Whiting, Jeffrey S. "Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1873.pdf.

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Books on the topic "Autonomous AI"

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Murphey, Yi Lu, Ilya Kolmanovsky, and Paul Watta, eds. AI-enabled Technologies for Autonomous and Connected Vehicles. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-06780-8.

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Anderson, Kence. Designing Autonomous AI. O'Reilly Media, Incorporated, 2022.

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Anderson, Kence. Designing Autonomous AI. O'Reilly Media, Incorporated, 2022.

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AI for Autonomous Vehicles. Wiley & Sons, Limited, John, 2024.

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AI for Autonomous Vehicles. Wiley & Sons, Incorporated, John, 2024.

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AI for Autonomous Vehicles. Wiley & Sons, Limited, John, 2024.

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AI for Autonomous Vehicles. Wiley & Sons, Incorporated, John, 2024.

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Burnett, Sarah. Autonomous Enterprise: Powered by AI. BCS Learning & Development Limited, 2022.

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Burnett, Sarah. Autonomous Enterprise: Powered by AI. BCS Learning & Development Limited, 2022.

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Burnett, Sarah. Autonomous Enterprise: Powered by AI. BCS Learning & Development Limited, 2022.

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Book chapters on the topic "Autonomous AI"

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Banafa, Ahmed. "AI and Autonomous Cars." In Transformative AI. River Publishers, 2024. http://dx.doi.org/10.1201/9781032669182-15.

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Slotte Dufva, Tomi. "Grasping AI Entanglements: Digital Feminism and Generative AI." In Humane Autonomous Technology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66528-8_11.

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AbstractCurrent machine learning models are advertised to create complex, lifelike or painterly images from diverse inputs, such as text, sketches or photos. These models can be seen as a continuation to the recent years’ developments within artificial intelligence. In this article, I aim to broaden the discussion around machine learning, art, aesthetics and ethics. How do these algorithms change the way we create and think of images? How, then, does it change our aesthetics? Furthermore, as many of these models require substantial amounts of resources, I am interested in how cost, both ecolog
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Aulinas, Josep, and Hanky Sjafrie. "AI for Autonomous Driving." In AI for Cars. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003099512-2-2.

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Patsariya, Sanjay. "AI-powered autonomous weapons." In Artificial Intelligence for Cyber Security and Industry 4.0. CRC Press, 2025. https://doi.org/10.1201/9781032657264-10.

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Mills, Stuart. "The Autonomous Choice Architect." In AI for Behavioural Science. CRC Press, 2022. http://dx.doi.org/10.1201/9781003203315-4.

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Plinge, Axel, Georgios Kontes, Sebastian Rietsch, and Christopher Mutschler. "Safe and Reliable AI for Autonomous Systems." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_13.

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AbstractA big challenge in implementing autonomous systems using reinforcement learning in a way to be used in the real world is to make them dependable, i.e., explainable and reliable. Automated and autonomous driving poses one of the biggest challenges to the development of artificial intelligence (AI), as it is technically demanding to solve the tasks involved in order to make a car act autonomously in real-world situations. However, unless autonomous systems become truly safe and dependable, they cannot be deployed in a real-world setup. Operating autonomous vehicles not only efficiently b
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Du, Mingfang. "From Autonomous Vehicle to AI City." In Autonomous Vehicle Technology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4143-6_10.

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Bartneck, Christoph, Christoph Lütge, Alan Wagner, and Sean Welsh. "Autonomous Vehicles." In An Introduction to Ethics in Robotics and AI. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51110-4_10.

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Witell, Lars, and Hannah Snyder. "Dishonesty Through AI: Can Robots Engage in Lying Behavior?" In Humane Autonomous Technology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66528-8_10.

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AbstractThis book chapter addresses how artificial intelligence (AI) engages in lying behavior in service encounters when customers interact with it when booking trips, investing money, or using customer service. The chapter concerns how AI learns from human behavior and how it practices the ethics of humans in service encounters. We identify three types of lying behavior from AI: (a) hallucinatory lying behavior, (b) manipulative lying behavior, and (c) directed lying behavior. Further, we show that mechanical AI, thinking AI, and feeling AI engage in these behaviors to different extents. By
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Kwik, Jonathan. "Adversarials: Anti-AI Countermeasures." In Lawfully Using Autonomous Weapon Technologies. T.M.C. Asser Press, 2024. http://dx.doi.org/10.1007/978-94-6265-631-4_6.

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Conference papers on the topic "Autonomous AI"

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Doda, Sanya, Karen Feigh, Richard Agbeyibor, Carmen Cortes, Jack Kolb, and Jose Magalhaes. "Human-AI Interaction in Autonomous Medical Evacuation Helicopters." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-282.

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The vertical flight industry is on its way to a transformative era, with autonomous technologies set to alter aerial vehicle operations. While it seems certain that fully autonomous helicopters will eventually be deployed for a variety of missions, some high-stakes situations—like medical evacuations (MEDEVAC)—will for the foreseeable future demand human participation in the form of Emergency Medical Care-giving Crew. This study describes the testbed built to run and investigate hypothetical future situations in which a helicopter is autonomously piloted while a human medic with no aviation tr
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Haj Youssef, Khaled Al, Yamen Zayegh, and Mohammad Alkhedher. "Autonomous AI-Controlled Mars Rover Robot." In 2024 Advances in Science and Engineering Technology International Conferences (ASET). IEEE, 2024. http://dx.doi.org/10.1109/aset60340.2024.10708660.

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Cowin, Jasmin. "Autonomous AI Agents – The Kraken Wakes." In 16th International Multi-Conference on Complexity, Informatics and Cybernetics. International Institute of Informatics and Cybernetics, 2025. https://doi.org/10.54808/imcic2025.01.20.

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C R, Komala, J. Raja, Sarange Shreepad Marotrao, Nalla Neelima, R. Sujatha, and K. Chidambarathanu. "AI algorithms for Autonomous Robotics Navigation." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877509.

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Parthasarathy, Kannan, Karthik Vaidhyanathan, Rudra Dhar, et al. "Engineering LLM Powered Multi-Agent Framework for Autonomous CloudOps." In 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE, 2025. https://doi.org/10.1109/cain66642.2025.00031.

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Pooja, R., Nikitha Elugubanti, Mary Shiba C, Kalai priya V, K. Sasirekha, and S. Rathana Sabapathy. "MediGuide AI using Machine Learning." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968033.

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Katta, Jithendra, Nikhil Reddy Kodumuru, and Swathi Katta. "InterACT: Enhancing Human-AI Interaction." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967771.

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Sharma, Anurag. "AIOps: the Autonomous Network Journey." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3a.3.

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AI-powered Network 5.0 uses AI and deep learning to predict and manage network issues, optimize support, and streamline processes to enable fast responses to change, promising autonomous, efficient, and reliable networks. Full-text article not available; see video presentation
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Obert, James, Rodrigo D. Trevizan, and Adrian Chavez. "Neuromorphic Computing in Autonomous Distributed Grid Control." In 2024 Conference on AI, Science, Engineering, and Technology (AIxSET). IEEE, 2024. https://doi.org/10.1109/aixset62544.2024.00025.

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Yu, Alian, Jian Kang, and Dan Lin. "AI-Enabled Efficient Traffic Scheduling for Autonomous Vehicles." In 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC). IEEE, 2025. https://doi.org/10.1109/ccnc54725.2025.10976106.

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Reports on the topic "Autonomous AI"

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Lohn, Andrew, Anna Knack, Ant Burke, and Krystal Jackson. Autonomous Cyber Defense. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/2022ca007.

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The current AI-for-cybersecurity paradigm focuses on detection using automated tools, but it has largely neglected holistic autonomous cyber defense systems — ones that can act without human tasking. That is poised to change as tools are proliferating for training reinforcement learning-based AI agents to provide broader autonomous cybersecurity capabilities. The resulting agents are still rudimentary and publications are few, but the current barriers are surmountable and effective agents would be a substantial boon to society.
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Konaev, Margarita, Ryan Fedasiuk, Jack Corrigan, et al. U.S. and Chinese Military AI Purchases. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/20200090.

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This data brief uses procurement records published by the U.S. Department of Defense and China’s People’s Liberation Army between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence. We find that the two militaries are prioritizing similar application areas, especially intelligent and autonomous vehicles and AI applications for intelligence, surveillance and reconnaissance.
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Joress, Howie. Driving U.S. Innovation in Materials and Manufacturing Using AI and Autonomous Labs. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.sp.1320.

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Brown, Jesse Daniel, Adrian Raudaschl, Prashant Kumar, Donald Johnson, and Tsukanov Vladislav Aleksandrovich. Anchoring Global Security: Autonomous Shipping with Mind Reading AI, GPT-core and MAMBA- core Agents, RAG-Fusion, AI Communities, Hive- AI, and the Human Psyche. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.nl5249y9.1.

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Brown, Jesse Daniel, Adrian Raudaschl, Prashant Kumar, Donald Johnson, and Tsukanov Vladislav Aleksandrovich. Anchoring Global Security: Autonomous Shipping with Mind Reading AI, GPT-core and MAMBA- core Agents, RAG-Fusion, AI Communities, Hive- AI, and the Human Psyche. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.nl5249y9.

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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Blanchard, Alexander, and Laura Bruun. Autonomous Weapon Systems and AI-enabled Decision Support Systems in Military Targeting: A Comparison and Recommended Policy Responses. Stockholm International Peace Research Institute, 2025. https://doi.org/10.55163/yqby3151.

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This report provides a comparative analysis of autonomous weapon systems (AWS) and artificial intelligence-enabled decision support systems (AI-DSS) used in in military targeting. It examines how both technologies shape human roles in decision making about the use of force, assesses their respective risks of unintended harm and analyses the key issues they raise under international law. By mapping the key similarities and differences between AWS and AI-DSS—particularly in their deployment across different phases of the targeting cycle—the report clarifies where existing policy frameworks apply
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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Dong, Jiqian, Sikai Chen, and Samuel Labi. Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317701.

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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