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Thematische Bibliographien / Agents and autonomous systems / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Agents and autonomous systems“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

Endriss, Ulle, Ann Nowé, Maria Gini, et al. "Autonomous agents and multiagent systems." AI Matters 7, no. 3 (2021): 29–37. http://dx.doi.org/10.1145/3511322.3511329.

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The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.

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Bhargava, Hemant K., and William C. Branley. "Simulating belief systems of autonomous agents." Decision Support Systems 14, no. 4 (1995): 329–48. http://dx.doi.org/10.1016/0167-9236(94)00036-r.

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3

Card, Alexander. "Structure Editors and Autonomous Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (2018): 288–89. http://dx.doi.org/10.1609/aiide.v14i1.13007.

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Rational agents are becoming prevalent in many domains, from data analysis to entertainment and games. The increased prevalence of agents has evolved new tools and techniques to work with and design new agents. One such technique is system simulation. Systems simulation is a technique an author can use to imitate tasks, processes, or systems, and in particular, agents. Systems simulation has a variety of uses, ranging from simulating ecological systems to entertainment, such as interactive narratives and digital games. However, many system simulators use specialized programming languages and r

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Košeckà, Jana, and Ruzena Bajcsy. "Discrete Event Systems for autonomous mobile agents." Robotics and Autonomous Systems 12, no. 3-4 (1994): 187–98. http://dx.doi.org/10.1016/0921-8890(94)90025-6.

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Patel, Anjali, Hava Patel, and Charmi Patel. "A Survey on the Evolution of Autonomous Agents: Trends, Challenges, and Future Directions." SPU- Journal of Science, Technology and Management Research II, no. 01 (2025): 58–68. https://doi.org/10.63766/spujstmr.24.000033.

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Over the past few decades, autonomous agents have undergone tremendous evolution, moving from rule based systems to highly adaptive, learning-driven architectures. These autonomously perceivable, reasoning, and acting agents have found use in robotics, healthcare, finance, and other fields. This survey provides a comprehensive overview of the evolution of autonomous agents, highlighting key technological advancements, emerging trends, and persistent challenges. We explore the role of deep reinforcement learning, multi-agent systems, neuro symbolic AI, and edge computing in enhancing agent auto

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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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JOHNSON, W. LEWIS, and BARBARA HAYES-ROTH. "The First Autonomous Agents Conference." Knowledge Engineering Review 13, no. 2 (1998): 137–42. http://dx.doi.org/10.1017/s0269888998002021.

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The First International Conference on Autonomous Agents brought together researchers concerned with implementing systems that perceive and act in dynamic, unpredictable environments, that coordinate interoperation among complementary component capabilities, and that perform significant jobs with a high degree of autonomy.

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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

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Dodig-Crnkovic, Gordana, and Mark Burgin. "A Systematic Approach to Autonomous Agents." Philosophies 9, no. 2 (2024): 44. http://dx.doi.org/10.3390/philosophies9020044.

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Agents and agent-based systems are becoming essential in the development of various fields, such as artificial intelligence, ubiquitous computing, ambient intelligence, autonomous computing, and intelligent robotics. The concept of autonomous agents, inspired by the observed agency in living systems, is also central to current theories on the origin, development, and evolution of life. Therefore, it is crucial to develop an accurate understanding of agents and the concept of agency. This paper begins by discussing the role of agency in natural systems as an inspiration and motivation for agent

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Valavanis, K. P. "Autonomous agents [Book Reviews]." IEEE Transactions on Robotics and Automation 15, no. 6 (1999): 1149. http://dx.doi.org/10.1109/tra.1999.817684.

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Langley, Pat, Ben Meadows, Mohan Sridharan, and Dongkyu Choi. "Explainable Agency for Intelligent Autonomous Systems." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (2017): 4762–63. http://dx.doi.org/10.1609/aaai.v31i2.19108.

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As intelligent agents become more autonomous, sophisti- cated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving veh

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Ur Rehman, Shafiq, and Aamer Nadeem. "An Approach to Model Based Testing of Multiagent Systems." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/925206.

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Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interac

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Such, Jose M., Agustín Espinosa, and Ana García-Fornes. "A survey of privacy in multi-agent systems." Knowledge Engineering Review 29, no. 3 (2013): 314–44. http://dx.doi.org/10.1017/s0269888913000180.

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AbstractPrivacy has been a concern for humans long before the explosive growth of the Internet. The advances in information technologies have further increased these concerns. This is because the increasing power and sophistication of computer applications offers both tremendous opportunities for individuals, but also significant threats to personal privacy. Autonomous agents and multi-agent systems are examples of the level of sophistication of computer applications. Autonomous agents usually encapsulate personal information describing their principals, and therefore they play a crucial role

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Benyon, D., and D. Murray. "Adaptive systems: from intelligent tutoring to autonomous agents." Knowledge-Based Systems 6, no. 4 (1993): 197–219. http://dx.doi.org/10.1016/0950-7051(93)90012-i.

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Zhang, Kun, Yoichiro Maeda, and Yasutake Takahashi. "Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (2011): 896–903. http://dx.doi.org/10.20965/jaciii.2011.p0896.

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Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward r

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Wolosky Muchulski, Vinicius, Italo Firmino da Silva, Heitor Henrique da Silva, et al. "Integrating Computer Vision with Embedded Intelligent Agents." Anais do Computer on the Beach 16 (May 27, 2025): 126–33. https://doi.org/10.14210/cotb.v16.p126-133.

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AbstractIn the current technological landscape, we are transitioning to alifestyle shaped and supported by advanced artificial intelligencesystems. In this context, there is a growing tendency to decentralizesuch systems, moving them to the edge, and placing more responsibilityon the devices that compose these systems. The field of Multi-Agent Systems offers a powerful paradigm for distributed artificialintelligence, where intelligent agents can incorporate a variety of AItechniques to make autonomous decisions. In this work, we proposean approach for implementing embedded autonomous intellige

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Talukdar, Sarosh N. "Collaboration rules for autonomous software agents." Decision Support Systems 24, no. 3-4 (1999): 269–78. http://dx.doi.org/10.1016/s0167-9236(98)00070-0.

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Deng, Zhenhua, and Yiguang Hong. "Multi-Agent Optimization Design for Autonomous Lagrangian Systems." Unmanned Systems 04, no. 01 (2016): 5–13. http://dx.doi.org/10.1142/s230138501640001x.

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In this paper, distributed optimization control for a group of autonomous Lagrangian systems is studied to achieve an optimization task with local cost functions. To solve the problem, two continuous-time distributed optimization algorithms are designed for multiple heterogeneous Lagrangian agents with uncertain parameters. The proposed algorithms are proved to be effective for those heterogeneous nonlinear agents to achieve the optimization solution in the semi-global sense, even with the exponential convergence rate. Moreover, simulation adequately illustrates the effectiveness of our optimi

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Dahlstedt, Palle, and Peter McBurney. "Musical Agents: Toward Computer-Aided Music Composition Using Autonomous Software Agents." Leonardo 39, no. 5 (2006): 469–70. http://dx.doi.org/10.1162/leon.2006.39.5.469.

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The authors, a composer and a computer scientist, discuss their collaborative research on the use of multiagent systems and their applicability to music and musical composition. They describe the development of software and techniques for the composition of generative music.

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DOVIER, AGOSTINO, ANDREA FORMISANO, and ENRICO PONTELLI. "Autonomous agents coordination: Action languages meet CLP() and Linda." Theory and Practice of Logic Programming 13, no. 2 (2012): 149–73. http://dx.doi.org/10.1017/s1471068411000615.

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AbstractThe paper presents a knowledge representation formalism, in the form of a high-levelAction Description Language (ADL)for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans.

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Wood, Sharon. "Representation and purposeful autonomous agents." Robotics and Autonomous Systems 49, no. 1-2 (2004): 79–90. http://dx.doi.org/10.1016/j.robot.2004.07.018.

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Wood, Sharon. "Representation and purposeful autonomous agents." Robotics and Autonomous Systems 51, no. 2-3 (2005): 217–28. http://dx.doi.org/10.1016/j.robot.2005.02.003.

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Langley, Pat. "Agents of Exploration and Discovery." AI Magazine 42, no. 4 (2022): 72–82. http://dx.doi.org/10.1609/aimag.v42i4.15089.

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Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the

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Langley, Pat. "Agents of Exploration and Discovery." AI Magazine 42, no. 4 (2022): 72–82. http://dx.doi.org/10.1609/aaai.12021.

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Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the

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Archibald, James K., Wynn C. Stirling, and Matthew S. Nokleby. "Socially Rational Models for Autonomous Agents." Open Cybernetics & Systemics Journal 2, no. 1 (2008): 122–41. http://dx.doi.org/10.2174/1874110x00802010122.

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Gelenbe, Erol, Khaled Hussain, and Varol Kaptan. "Simulating autonomous agents in augmented reality." Journal of Systems and Software 74, no. 3 (2005): 255–68. http://dx.doi.org/10.1016/j.jss.2004.01.016.

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ADACHER, LUDOVICA, ALESSANDRO AGNETIS, and CARLO MELONI. "Autonomous agents architectures and algorithms in flexible manufacturing systems." IIE Transactions 32, no. 10 (2000): 941–51. http://dx.doi.org/10.1080/07408170008967452.

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Wong, Johnny S. K., and Armin R. Mikler. "Intelligent mobile agents in large distributed autonomous cooperative systems." Journal of Systems and Software 47, no. 2-3 (1999): 75–87. http://dx.doi.org/10.1016/s0164-1212(99)00027-8.

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Montagna, Sara, Daniel Castro Silva, Pedro Henriques Abreu, Marcia Ito, Michael Ignaz Schumacher, and Eloisa Vargiu. "Autonomous agents and multi-agent systems applied in healthcare." Artificial Intelligence in Medicine 96 (May 2019): 142–44. http://dx.doi.org/10.1016/j.artmed.2019.02.007.

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Prada, Rui. "Autonomous agents to accelerate extended reality testing." EU Research - The necessity of science in uncertain times Summer 2022, no. 31 (2022): 42–43. http://dx.doi.org/10.56181/anyp4112.

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Extended reality systems (XR) are used in a wide range of sectors, yet they need to be tested before they can reach the commercial market. We spoke to Rui Filipe Fernandes Prada about the work of the iv4XR project in developing autonomous testing agents that promise to bring significant benefits to developers.

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Botchkaryov, A. "AUTONOMOUS DECENTRALIZED COMPUTER NETWORK MONITORING SYSTEM BASED ON SOFTWARE AGENTS." Computer systems and network 5, no. 1 (2023): 1–7. http://dx.doi.org/10.23939/csn2023.01.001.

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Espinosa-Leal, Leonardo, Anthony Chapman, and Magnus Westerlund. "Autonomous Industrial Management via Reinforcement Learning." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8427–39. http://dx.doi.org/10.3233/jifs-189161.

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Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper, we indicate the distinctions between automated and autonomous systems as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss usi

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Gaurav Samdani, Yawal Dixit, and Ganesh Vishwanathan. "Leveraging LangGraph and AutoGen for Agentic AI Frameworks." World Journal of Advanced Engineering Technology and Sciences 8, no. 2 (2023): 402–11. https://doi.org/10.30574/wjaets.2023.8.2.0068.

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This research examines how LangGraph and AutoGen improve Agentic AI models by enabling improved autonomous functioning in dynamic environments. Researchers examine LangGraph's language-based system and AutoGen's generative model as independently working tools for agent autonomous performance in intricate situations. Our study uses quality-benchmarking data and test simulations to examine modeling effects on AI agents' behavior and decision-making. The study shows that LangGraph boosts language understanding effectiveness while AutoGen improves the system's ability to adjust decisions swiftly i

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Botchkaryov, A. "METHOD FOR DECENTRALIZED CONTROL OF ADAPTIVE DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS." Computer systems and network 5, no. 1 (2023): 8–19. http://dx.doi.org/10.23939/csn2023.01.008.

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The problem of monitoring a computer network under conditions of limitations on the use of system resources and high requirements for the survivability of the monitoring system has been considered. An autonomous decentralized computer network monitoring system has been developed, consisting of a team of software agents. Each agent can operate in two modes: main mode and monitoring system management console mode. In the main mode, the agent collects information about the computer network. In management console mode, the agent provides the user with access to information collected by all agents

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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

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Rajubhai Golani, Rajeshkumar. "The Rise of AI Agents: Transforming Enterprise Automation." International Journal of Advances in Engineering and Management 7, no. 4 (2025): 132–39. https://doi.org/10.35629/5252-0704132139.

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Artificial intelligence is undergoing a paradigm shift, evolving from rule-based systems into autonomous agents capable of end-to-end task execution and complex decision-making. This transformation represents what industry leaders call the "third wave" of enterprise automation, where AI systems become active participants in business processes. Modern AI agents combine large language models with multi-agent frameworks that enable specialized functions, self-optimization through feedback loops, and autonomous learning. These agents excel in real-time decision-making across multiple data sources,

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PELACHAUD, CATHERINE, and ISABELLA POGGI. "Multimodal embodied agents." Knowledge Engineering Review 17, no. 2 (2002): 181–96. http://dx.doi.org/10.1017/s0269888902000218.

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1 Believable interactive embodied agentsAmong the goals of research on autonomous agents one important aim is to build believable interactive embodied agents that are apt to application to friendly interfaces in e-commerce, tourist and service query systems, entertainment (e.g. synthetic actors) and education (pedagogical agents, agents for help and instruction to the hearing impaired).

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Monteserin, Ariel, and Analía Amandi. "Argumentation–based negotiation planning for autonomous agents." Decision Support Systems 51, no. 3 (2011): 532–48. http://dx.doi.org/10.1016/j.dss.2011.02.016.

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Durlik, Irmina, Tymoteusz Miller, Ewelina Kostecka, Polina Kozlovska, and Wojciech Ślączka. "Enhancing Safety in Autonomous Maritime Transportation Systems with Real-Time AI Agents." Applied Sciences 15, no. 9 (2025): 4986. https://doi.org/10.3390/app15094986.

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The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, efficiency, and sustainability of autonomous maritime systems. Following a structured literature review, we examine the architecture of real-time AI agents, including sensor integration, communication systems, and computational infrastructure. We distinguish maritime AI agents from conventional systems by emphasizing their specialized functions, rea

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Casadei, Roberto, Gianluca Aguzzi, and Mirko Viroli. "A Programming Approach to Collective Autonomy." Journal of Sensor and Actuator Networks 10, no. 2 (2021): 27. http://dx.doi.org/10.3390/jsan10020027.

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Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further

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Wai, Sim Keng, Cheah WaiShiang, Muhammad Asyraf Bin Khairuddin, Yanti Rosmunie Binti Bujang, Rahmat Hidayat, and Celine Haren Paschal. "Autonomous Agents in 3D Crowd Simulation Through BDI Architecture." JOIV : International Journal on Informatics Visualization 5, no. 1 (2021): 1. http://dx.doi.org/10.30630/joiv.5.1.371.

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Agent based simulation (ABS) is a paradigm to modelling systems included of autonomous and interacting agents. ABS has been tremendous growth and used by researchers in the social sciences to study socio-environmental complex systems. To date, various platforms have been introduced for agent-based social simulation. They are rule based in any logic, python based in SPADE and etc. Although those platforms have been introduced, there is still an insufficient to develop a crowd simulation in 3D platform. Having a 3D platform is needed to enabling the crowd simulation for training purposes. Howeve

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Schaub Jr., Gary. "Controlling the Autonomous Warrior." Journal of International Humanitarian Legal Studies 10, no. 1 (2019): 184–202. http://dx.doi.org/10.1163/18781527-01001007.

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The challenges posed by weapons with autonomous functions are not a tabula rasa. The capabilities of both State principals and military agents to control and channel violence for political purposes have improved across the centuries as technology has increased the range and lethality of weapons as well as the scope of warfare. The institutional relations between principals and agents have been adapted to account for, and take advantage of, these developments. Air forces encompass one realm where distance, speed, and lethality have been subjected to substantial and effective control. Air forces

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Fuchs, Andrew, Andrea Passarella, and Marco Conti. "Compensating for Sensing Failures via Delegation in Human–AI Hybrid Systems." Sensors 23, no. 7 (2023): 3409. http://dx.doi.org/10.3390/s23073409.

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Given the increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g., perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human–AI teaming case where a managing agent is tasked with identifying when to perform a delegated assignm

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SUGIMURA, Nobuhiro, and Koji IWAMURA. "2201 DYNAMIC INTEGRATED PROCESS PLANNING AND PRODUCTION CONTROL IN FLEXIBLE MANUFACTURING SYSTEMS WITH AUTONOMOUS AGENTS." Proceedings of Manufacturing Systems Division Conference 2007 (2007): 37–38. http://dx.doi.org/10.1299/jsmemsd.2007.37.

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Yamamoto, Masahito, Takashi Kawakami, and Keitaro Naruse. "Special Issue on Machine Learning for Robotics and Swarm Systems." Journal of Robotics and Mechatronics 31, no. 4 (2019): 519. http://dx.doi.org/10.20965/jrm.2019.p0519.

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In recent years, machine-learning applications have been rapidly expanding in the fields of robotics and swarm systems, including multi-agent systems. Swarm systems were developed in the field of robotics as a kind of distributed autonomous robotic systems, imbibing the concepts of the emergent methodology for extremely redundant systems. They typically consist of homogeneous autonomous robots, which resemble living animals that build swarms. Machine-learning techniques such as deep learning have played a remarkable role in controlling robotic behaviors in the real world or multi-agents in the

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Langley, Pat. "Open-World Learning for Radically Autonomous Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13539–43. http://dx.doi.org/10.1609/aaai.v34i09.7078.

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In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approa

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Seeber, Isabella, Lena Waizenegger, Stefan Seidel, Stefan Morana, Izak Benbasat, and Paul Benjamin Lowry. "Collaborating with technology-based autonomous agents." Internet Research 30, no. 1 (2020): 1–18. http://dx.doi.org/10.1108/intr-12-2019-0503.

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PurposeThis article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work.Design/methodology/approachThe panelists (Drs Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision-making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis

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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 resp

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Kaelbling, Leslie Pack. "Foundations of learning in autonomous agents." Robotics and Autonomous Systems 8, no. 1-2 (1991): 131–44. http://dx.doi.org/10.1016/0921-8890(91)90018-g.

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Khoshnoud, Farbod, Ibrahim I. Esat, Clarence W. de Silva, and Marco B. Quadrelli. "Quantum Network of Cooperative Unmanned Autonomous Systems." Unmanned Systems 07, no. 02 (2019): 137–45. http://dx.doi.org/10.1142/s2301385019500055.

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A quantum network may be realized by the entanglement of particles communicated by qubits between quantum computers, where the entangled photons of light are transferred for communication purposes. This technology has been proven to be feasible experimentally through free-space distribution of entangled photon pairs. Sending photons of light through nonlinear crystals produces correlated photon pairs, by splitting each photon into two half particles with each particle having the same level of energy, which results in entangled pairs. This entanglement is represented by photons, having both eit

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