Relevant bibliographies by topics / Autonomous AI Agents

Contents

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

Academic literature on the topic 'Autonomous AI Agents'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Autonomous AI Agents"

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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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FRANKLIN, STAN. "AUTONOMOUS AGENTS AS EMBODIED AI." Cybernetics and Systems 28, no. 6 (1997): 499–520. http://dx.doi.org/10.1080/019697297126029.

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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 propel the creation of such autonomous business models and, by extension, the digital transformation of industrial conglomerates.
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Ziemke, Tom. "Adaptive Behavior in Autonomous Agents." Presence: Teleoperators and Virtual Environments 7, no. 6 (1998): 564–87. http://dx.doi.org/10.1162/105474698565947.

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.
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Maes, Pattie. "Modeling Adaptive Autonomous Agents." Artificial Life 1, no. 1_2 (1993): 135–62. http://dx.doi.org/10.1162/artl.1993.1.1_2.135.

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One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of Artificial Intelligence (AI), which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach. It attempts to extract its main ideas, evaluates what contributions have been made so far, and identifies its current limitations and open problems.
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SUMANTH REDDY, SHIVA. "AI Agents: Agent GPT." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50557.

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Abstract - Agent GPT is an advanced autonomous AI system designed to simulate human-like reasoning and task execution through the deployment of AI agents. Unlike traditional large language models that respond passively to user prompts, Agent GPT can plan, iterate, and execute multi-step goals with minimal human intervention. Each agent operates based on a defined objective, breaking it down into smaller tasks, leveraging APIs, tools, or internet access to gather information, and adapting dynamically to changing conditions. The system is often built on top of language models such as GPT-4, and incorporates features like memory, tool use, and recursive task execution to complete complex workflows. Agent GPT is used in applications ranging from automated research and customer support to software development and marketing strategy generation. Its core innovation lies in enabling models to act not just as conversational tools, but as autonomous problem solvers capable of taking initiative, learning from context, and optimizing toward defined goals
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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 stands as a testament to the proficiency and versatility of AI. What makes 'HELIX' especially groundbreaking is its user-friendly approach, ensuring anyone, regardless of their technical background, can harness its power. As we embrace the dawn of this technological era, 'HELIX' not only showcases the potential of today's AI capabilities but also paves the way for future innovations, promising an expansive horizon for AI-driven solutions across myriad domains.
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Nguyen Thanh, Binh, Ha Xuan Son, and Diem Thi Hong Vo. "Blockchain: The Economic and Financial Institution for Autonomous AI?" Journal of Risk and Financial Management 17, no. 2 (2024): 54. http://dx.doi.org/10.3390/jrfm17020054.

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This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating digital economic and financial institutions, permitting AI to engage with these institutions through the management of private keys. This extends AI’s capabilities to form and execute contracts, participate in marketplaces, and utilize financial services autonomously. The paper encourages further research on AI as a general-purpose technology and blockchain as an institutional technology that can unlock the full capabilities of autonomous AI agents.
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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, and efficient autonomous systems. Specifically, the research focuses on the application of AI in decision-making and environment perception, while utilizing Blockchain to secure data integrity, enable decentralized governance, and enhance trust among autonomous agents. We propose an integrated system where AI-driven autonomous agents interact with a decentralized blockchain network to log decisions, share data, and operate transparently. The proposed solution is evaluated through simulations, highlighting the benefits and challenges of such integration. The results show that blockchain can improve transparency and security in autonomous systems, while AI enhances decision-making in real-time. However, challenges related to scalability, transaction latency, and privacy concerns need further investigation. This research lays the foundation for the development of next-generation decentralized autonomous systems, with implications for various industries, including transportation, logistics, and robotics.
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Kusuma Kumar Parimi,, A. Santhosh. "Adaptation and Learning in AI Agents." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45372.

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Customer support process has seen a transformational shift in recent years. With the rapid proliferation of Artificial Integrating (AI), the human driven customer support has leapfrogged into automated customer services & support. The field of Artificial Intelligence (AI) has seen phenomenal rise in the development of agents' capability right from autonomous decision-making and problem-solving abilities to adapt to changing environments. Their proficiency in learning from experience and adaptation greatly influences their effectiveness. They both drive improvement over time and insure response to dynamic customer needs and conditions. Adaptation and learning also permit strategies to be revised in light of new data provided to them. This paper explores the bases and techniques of adaptation and learning in AI agents, focusing on reinforcement learning, supervised learning, unsupervised learning, and the combination. In addition, it also addresses challenges like overfitting, exploration-exploitation tradeoffs and computational economics. The role of these processes in various AI applications, including robotics, natural language processing and autonomous systems help industry reduce dependency on manual effort and improve efficiency to drive growth. Keywords-Adaptation, Learning, AI agents, Reinforcement learning, Supervised learning, Autonomous systems.
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Dissertations / Theses on the topic "Autonomous AI Agents"

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Esbjörnsson, Jimmy. "EMO - A Computational Emotional State Module : Emotions and their influence on the behaviour of autonomous agents." Thesis, Linköping University, Department of Science and Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9090.

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<p>Artificial intelligence (AI) is already a fundamental component of computer games. In this context is emotions a growing part in simulating real life. The proposed emotional state module, provides a way for the game agents to select an action in real-time virtual environments. The modules function has been tested with the open-source strategy game ORTS. This thesis proposes a new approach for the design of an interacting network, similar to a spreading activation system, of emotional states that keeps track of emotion intensities changing and interacting over time. The network of emotions can represent any number of persisting states, such as moods, emotions and drives. Any emotional signal can affect every state positively or negatively. The states' response to emotional signals are influenced by the other states represented in the network. The network is contained within an emotional state module. This interactions between emotions are not the focus of much research, neither is the representation model. The focus tend to be on the mechanisms eliciting emotions and on how to express the emotions.</p>
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Nogueira, Yuri Lenon Barbosa. "IntegraÃÃo Mente e Ambiente para a GeraÃÃo de Comportamentos Emergentes em Personagens Virtuais AutÃnomos AtravÃs da EvoluÃÃo de Redes Neurais Artificiais." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12686.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior<br>O senso de imersÃo do usuÃrio em um ambiente virtual requer nÃo somente alta qualidade visual grÃfica, mas tambÃm comportamentos adequados por parte dos personagens virtuais, isto Ã, com movimentos e aÃÃes que correspondam Ãs suas caracterÃsticas fÃsicas e aos eventos que ocorrem em seu meio. Nesse contexto, percebe-se o papel fundamental desempenhado pelo modo como os agentes se comportam em aplicaÃÃes de RV. O problema que permanece em aberto Ã: âComo obter comportamentos autÃnomos naturais e realistas de personagens virtuais?â. Um agente à dito autÃnomo se ele for capaz de gerar suas prÃprias normas (do grego autos, "a si mesmo", e nomos, "norma", "ordem"). Logo, autonomia implica em aÃÃes realizadas por um agente que resultam da estreita interaÃÃo entre suas dinÃmicas internas e os eventos ocorrendo no ambiente ao seu redor, ao invÃs de haver um controle externo ou uma especificaÃÃo de respostas em um plano prÃ-definido. Desse modo, um comportamento autÃnomo deveria refletir os detalhes da associaÃÃo entre o personagem e o ambiente, implicando em uma maior naturalidade e realismo nos movimentos. Assim, chega-se à proposta de que um comportamento à considerado natural se ele mantÃm coerÃncia entre o corpo do personagem e o ambiente ao seu redor. Para um observador externo, tal coerÃncia à percebida como comportamento inteligente. Essa noÃÃo resulta do atual debate, no campo da InteligÃncia Artificial, sobre o significado da inteligÃncia. Baseado nas novas tendÃncias surgidas dessas discussÃes, argumenta-se que o nÃvel de coerÃncia necessÃrio a um comportamento natural apenas pode ser alcanÃado atravÃs de tÃcnicas de emergÃncia. AlÃm da defesa conceitual da abordagem emergentista para a geraÃÃo de comportamento de personagens virtuais, este estudo apresenta novas tÃcnicas para a implementaÃÃo dessas ideias. Entre as contribuiÃÃes, està a proposta de um novo processo de codificaÃÃo e evoluÃÃo de Redes Neurais Artificiais que permite o desenvolvimento de controladores para explorar as possibilidades da geraÃÃo de comportamentos por emergÃncia. TambÃm à explorada a evoluÃÃo sem objetivo, atravÃs da simulaÃÃo da reproduÃÃo sexuada de personagens. Para validar a tese, foram desenvolvidos experimentos envolvendo um robà virtual. Os resultados apresentados mostram que a auto-organizaÃÃo de um sistema à de fato capaz de produzir um acoplamento Ãntimo entre agente e ambiente. Como consequÃncia da abordagem adotada, foram obtidos comportamentos bastante coerentes com as capacidades dos personagens e as condiÃÃes ambientais, com ou sem descriÃÃo de objetivos. Os mÃtodos propostos se mostraram sensÃveis a modificaÃÃes do ambiente e a modificaÃÃes no sensoriamento do robÃ, comprovando robustez ao gerar cÃrtices visuais funcionais, seja com sensores de proximidade, seja com cÃmeras virtuais, interpretando seus pixels. Ressalta-se tambÃm a geraÃÃo de diferentes tipos de comportamentos interessantes, sem qualquer descriÃÃo de objetivos, nos experimentos envolvendo reproduÃÃo simulada.<br>The userâs sense of immersion requires not only high visual quality of the virtual environment, but also accurate simulations of dynamics to ensure the reliability of the experience. In this context, the way the characters behave in a virtual environment plays a fundamental role. The problem that remains open is: âWhat needs to be done for autonomous virtual characters to display natural/realistic behaviors?â. A behavior is considered autonomous when the actions performed by the agent result from a close interaction between its internal dynamics and the circumstantial events in the environment, rather than from external control or specification dictated by a predefined plan. Thus, an autonomous behavior should reflect the details of the association between the character and its environment, resulting in greater naturalness and realistic movements. Therefore, it is proposed that the behavior is considered natural if it maintains coherence between the characterâs body and the environment surrounding it. To an external observer, such coherence is perceived as intelligent behavior. This notion of intelligent behavior arose from a current debate, in the field of Artificial Intelligence, about the meaning of intelligence. Based on the new trends that came out from those discussions, it is argued that the level of coherence required for natural behavior in complex situations can only be achieved through emergence. In addition to the conceptual support of the emergentist approach to generating behavior of virtual characters, this study presents new techniques for implementing those ideas. A contribution of this work is a novel technique for the enconding and evolution of Artificial Neural Networks, which allows the development of controllers to explore the possibilities of generating behaviors through emergence. Evolution without objective description is also explored through the simulation of sexual reproduction of characters. In order to validate the theory, experiments involving a virtual robot were developed. The results show that self-organization of a system is indeed able to produce an intimate coupling between agent and environment. As a consequence of the adopted approach, it were achieved behaviors quite consistent with the characterâs capabilities and environmental conditions, with or without description of objectives. The proposed methods were sensitive to changes in the environment and in the robotâs sensory apparatus, proving robustness on generating functional visual cortices, either with proximity sensors or with virtual cameras, interpreting its pixels. It is also emphasized the generation of different types of interesting behaviors, without any description of objectives, in experiments involving simulated reproduction.
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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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Dinerstein, Jonathan J. "Improving and Extending Behavioral Animation Through Machine Learning." BYU ScholarsArchive, 2005. https://scholarsarchive.byu.edu/etd/310.

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Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated through novel machine learning schemes. Problem 1 is addressed by using fast regression techniques to quickly approximate a cognitive model. Problem 2 is addressed by a novel multi-level technique composed of custom machine learning methods to gather salient knowledge with which to guide decision making. Finally, Problem 3 is addressed through programming-by-demonstration, allowing a non technical user to quickly and intuitively specify agent behavior.
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Books on the topic "Autonomous AI Agents"

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Murphy, Robin R. An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, 2000.

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Designing Sociable Robots (Intelligent Robotics and Autonomous Agents). The MIT Press, 2004.

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Designing Sociable Robots (Intelligent Robotics and Autonomous Agents). The MIT Press, 2002.

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Strategic Negotiation in Multiagent Environments (Intelligent Robotics and Autonomous Agents). The MIT Press, 2001.

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Mahankali, Srinivas, Deepika M, Vijay Cuddapah, and Amitendra Srivastava. AI & ML - Powering the Agents of Automation: Demystifying, IOT, Robots, ChatBots, RPA, Drones & Autonomous Cars- The new workforce led Digital ... by AI & ML and secured through Blockchain. BPB Publications, 2019.

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Mohan Kumar, Dr S. Artificial Intelligence: Foundations, Applications, and the Generative Future. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2025. https://doi.org/10.47716/978-93-92090-63-9.

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Artificial Intelligence (AI) is no longer confined to laboratories and theoretical models; it is now a driving force behind the digital transformation of society. Artificial Intelligence: Foundations, Applications, and the Generative Future provides an in-depth journey through AI’s essential principles, its wide-ranging applications, and the revolutionary impact of generative technologies. The book methodically builds knowledge from classical AI concepts like intelligent agents, search strategies, and knowledge representation, to advanced learning models including neural networks and machine learning algorithms. It culminates in an exploration of Generative AI—highlighting its transformative role in Industry 5.0, autonomous systems, and creative industries. Designed for both academic and professional audiences, this work offers not just understanding, but vision—preparing readers to navigate and shape the next frontier of AI evolution. Keywords: Artificial Intelligence, Machine Learning, Knowledge Representation, Neural Networks, Generative AI, Deep Learning, Industry 5.0, Intelligent Systems, Future of AI, Digital Transformation
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Brachman, Ronald J., and Hector J. Levesque. Machines like Us. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/14299.001.0001.

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How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise. It's sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what's happening and find a workaround. In Machines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today's AI systems. Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.
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Book chapters on the topic "Autonomous AI Agents"

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Cronin, Irena. "Autonomous AI Agents: Decision-Making, Data, and Algorithms." In Understanding Generative AI Business Applications. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0282-9_11.

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Remagnino, Paolo, Graeme A. Jones, and Ndedi Monekosso. "Reasoning about Dynamic Scenes Using Autonomous Agents." In AI*IA 2001: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45411-x_21.

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Rasheed, Zeeshan, Muhammad Waseem, Malik Abdul Sami, et al. "Autonomous Agents in Software Development: A Vision Paper." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72781-8_2.

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AbstractLarge Language Models (LLM) are reshaping the field of Software Engineering (SE). They enable innovative methods for executing many SE tasks, including automation of entire process of Software Development Life Cycle (SDLC). However, only a limited number of existing works have thoroughly explored the potential of LLM based AI agents to automate the entire lifecycle in SE. In this paper, we demonstrate the success of our initial efforts in automating the entire lifecycle autonomously based on given software specification as input, which has shown remarkable efficiency and significantly reduced development time. Our preliminary results suggest that the careful implementation of AI agents can enhance the development lifecycle. We aim to streamline the SDLC by integrating all phases into an AI-driven chat interface, enhancing efficiency and transparency. Furthermore, we seek to enhance collaboration, creating an environment where stakeholders from various backgrounds can contribute, review, and refine ideas and requirements in real-time. This forward-looking direction guarantees to redefine the paradigms of SE and also make software creation more inclusive, collaborative, and efficient.
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Falcone, Rino, and Cristiano Castelfranchi. "Tuning the Collaboration Level with Autonomous Agents: A Principled Theory." In AI*IA 2001: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45411-x_22.

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Sarkadi, Stefan, Peidong Mei, and Edmond Awad. "Should My Agent Lie for Me? Public Moral Perspectives on Deceptive AI." In Autonomous Agents and Multiagent Systems. Best and Visionary Papers. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56255-6_9.

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Belle, Vaishak, Michael Fisher, Alessandra Russo, Ekaterina Komendantskaya, and Alistair Nottle. "Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges." In Autonomous Agents and Multiagent Systems. Best and Visionary Papers. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56255-6_10.

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Reichberg, Gregory M., and Henrik Syse. "Applying AI on the Battlefield: The Ethical Debates." In Robotics, AI, and Humanity. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_12.

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AbstractBecause lethal autonomous weapon systems (LAWS) are designed to make targeting decisions without the direct intervention of human agents (who are “out of the killing loop”), considerable debate has arisen on whether this mode of autonomous targeting should be deemed morally permissible. Surveying the contours of this debate, the authors first present a prominent ethical argument that has been advanced in favor of LAWS, namely, that AI-directed robotic combatants have an advantage over their human counterparts, insofar as the former operate solely on the basis of rational assessment, while the latter are often swayed by emotions that conduce to poor judgment. Several counter arguments are then presented, inter alia, (1) that emotions have a positive influence on moral judgment and are indispensable to it; (2) that it is a violation of human dignity to be killed by a machine, as opposed to being killed by a human being; and (3) that the honor of the military profession hinges on maintaining an equality of risk between combatants, an equality that would be removed if one side delegates its fighting to robots. The chapter concludes with a reflection on the moral challenges posed by human-AI teaming in battlefield settings, and how virtue ethics provides a valuable framework for addressing these challenges.
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Yaman, Sinem Getir, Charlie Burholt, Maddie Jones, Radu Calinescu, and Ana Cavalcanti. "Specification and Validation of Normative Rules for Autonomous Agents." In Fundamental Approaches to Software Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30826-0_13.

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AbstractA growing range of applications use autonomous agents such as AI and robotic systems to perform tasks deemed dangerous, tedious or costly for humans. To truly succeed with these tasks, the autonomous agents must perform them without violating the social, legal, ethical, empathetic, and cultural (SLEEC) norms of their users and operators. We introduce SLEECVAL, a tool for specification and validation of rules that reflect these SLEEC norms. Our tool supports the specification of SLEEC rules in a DSL [1] we co-defined with the help of ethicists, lawyers and stakeholders from health and social care, and uses the CSP refinement checker FDR4 to identify redundant and conflicting rules in a SLEEC specification. We illustrate the use of SLEECVAL for two case studies: an assistive dressing robot, and a firefighting drone.
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Skobelev, Petr. "Towards Autonomous AI Systems for Resource Management: Applications in Industry and Lessons Learned." In Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94580-4_2.

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Kawahara, Tatsuya, Hiroshi Saruwatari, Ryuichiro Higashinaka, Kazunori Komatani, and Akinobu Lee. "Spoken Dialogue Technology for Semi-Autonomous Cybernetic Avatars." In Cybernetic Avatar. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3752-9_3.

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AbstractSpeech technology has made significant advances with the introduction of deep learning and large datasets, enabling automatic speech recognition and synthesis at a practical level. Dialogue systems and conversational AI have also achieved dramatic advances based on the development of large language models. However, the application of these technologies to humanoid robots remains challenging because such robots must operate in real time and in the real world. This chapter reviews the current status and challenges of spoken dialogue technology for communicative robots and virtual agents. Additionally, we present a novel framework for the semi-autonomous cybernetic avatars investigated in this study.
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Conference papers on the topic "Autonomous AI Agents"

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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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Jabbar, Haidar, Samir Al-Janabi, and Francis Syms. "Securing Autonomous Vehicles with Smart AI Security Agents." In 2025 International Conference on New Trends in Computing Sciences (ICTCS). IEEE, 2025. https://doi.org/10.1109/ictcs65341.2025.10989346.

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Mauri, Marcel, Ömer Ibrahim Erduran, and Mirjam Minor. "Robust BDI Agents in Autonomous Mobility on Demand." In 2024 International Conference on AI x Data and Knowledge Engineering (AIxDKE). IEEE, 2024. https://doi.org/10.1109/aixdke63520.2024.00016.

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Ohagi, Masaya. "Polarization of Autonomous Generative AI Agents Under Echo Chambers." In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wassa-1.10.

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Samaddar, Ankita, Nicholas Potteiger, and Xenofon Koutsoukos. "Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents." In 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC). IEEE, 2025. https://doi.org/10.1109/icaic63015.2025.10849024.

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Meenakshi, S. Pavaimalar, Rama Prabha K. P, S. Ravi, N. Kumaran, and S. B. Priya. "AI-Driven Autonomous Robots for Search and Rescue Operations in Disaster Zones." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011570.

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Mirzayi, Sahar, and Erfan Talajouran. "From Decision Support to Decision Making: Autonomous AI Agents Shaping the Marketing." In 2025 11th International Conference on Web Research (ICWR). IEEE, 2025. https://doi.org/10.1109/icwr65219.2025.11006187.

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Shifare, Hagos, Madhu Shukla, and Nishant Kothari. "AI-Powered Autonomous Remote Access and Scheduling System to High-Performance Computing Resources." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011624.

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Chigurupati, Mourya, Rajesh Kumar Malviya, Arvind Reddy Toorpu, and Karanveer Anand. "AI Agents for Cloud Reliability: Autonomous Threat Detection and Mitigation Aligned with Site Reliability Engineering Principles." In 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC). IEEE, 2025. https://doi.org/10.1109/icaic63015.2025.10849322.

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Charanya, P., Santhos Raj B S, Naveen Kumar T, and Pradheeban A. "Marching Forward: Redefining Human-Machine Interactions in Conversational AI Through Hybrid Intelligence, Blockchain Security, and Autonomous Agents." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859659.

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Reports on the topic "Autonomous AI Agents"

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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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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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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—such as ChatGPT or Gemini—capable of producing text, images, or decisions based on prompts. However, it is the third wave, embodied by AI agents, that poses the greatest challenge: software capable of autonomous action, making decisions on behalf of users, interacting across platforms, and executing tasks with minimal human oversight.
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Toner, Helen, John Bansemer, Kyle Crichton, et al. Through the Chat Window and Into the Real World: Preparing for AI Agents. Center for Security and Emerging Technology, 2024. http://dx.doi.org/10.51593/20240034.

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Computer scientists have long sought to build systems that can actively and autonomously carry out complicated goals in the real world—commonly referred to as artificial intelligence "agents." Recently, significant progress in large language models has fueled new optimism about the prospect of building sophisticated AI agents. This CSET-led workshop report synthesizes findings from a May 2024 workshop on this topic, including what constitutes an AI agent, how the technology is improving, what risks agents exacerbate, and intervention points that could help.
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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 trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.
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You might also be interested in the extended bibliographies on the topic 'Autonomous AI Agents' for particular source types:
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