AgentAI: A comprehensive survey on autonomous agents in distributed AI for industry 4.0
Open accessAbstract
AgentAI represents a transformative approach within distributed Artificial Intelligence (AI) in which autonomous agents work either individually or collaboratively in decentralized environments to address challenging problems. AgentAI enhances scalability, robustness, and flexibility by utilizing advanced communication, learning, and decision-making capabilities, making it integral to diverse applications in Industry 4.0. The ability of AI systems to interpret sensory data in open-world environments has seen significant advancements in recent years. This progress emphasizes the need to move beyond reductionist approaches and embrace more embodied and cohesive systems, which integrate foundational models into agent-driven actions. Existing surveys often focus on isolated domains or specific autonomy levels, lacking a cohesive analysis that spans the full spectrum of AgentAI development in Industry 4.0. This survey explicitly fills this gap by introducing a multi-domain taxonomy and by systematically analyzing both non-autonomous and fully autonomous AgentAI systems, offering a comprehensive synthesis not previously available in the literature. Additionally, the paper extends the discussion to Industry 5.0 and 6.0, exploring the evolution of AgentAI from automation to collaboration and, ultimately, to fully autonomous systems. This comprehensive analysis highlights the potential of AgentAI in driving industries toward a more efficient, sustainable, and adaptable future.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multi-domain agents engage in real-time decision-making, minimize costs, make better use of resources, produce intelligible outputs, and plan and execute tasks under uncertainty.
“Agents also engage in realtime decision-making, minimize costs, make better use of resources, produce intelligible outputs, and plan and execute tasks under uncertainty [36,135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Models like LLMs and VLMs enable adaptive decision-making in agentic decision-making when processes use combinations of text, images, and audio, thus enabling an agentic system to demonstrate embodied reasoning and task execution towards articulated goals.
“Models like LLMs and VLMs enable adaptive decision-making in agentic decision-making when processes use combinations of text, images, and audio, thus enabling an agentic system to demonstrate embodied reasoning and task execution towards articulated goals [33,135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Transparency for models like AMBY and Drawcto is tested through user-facing clarity, Constitutional AI models using predefined ethical principles and Hybrid Transparency Models are tested through open-source governance components and external audits.
“Transparency for models like AMBY and Drawcto [135] is tested through user-facing clarity, Constitutional AI models [44] using predefined ethical principles and Hybrid Transparency Models [37] are tested through open-source governance components and external audits.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Traditional AI systems generally work on structured, static inputs and produce fixed or predefined outputs.
“Traditional AI systems generally work on structured, static inputs and produce fixed [71] or predefined outputs [135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Biased and unfair outcomes from agentic AI may affect trust and fairness.
“Finally, when the data are used by agentic AI, they may often be broken, incomplete, or poor in quality, so this also leads to biased and unfair outcomes, and that may affect trust and fairness [135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Limited design, test, and deployment support for agent design, testing and deploy environments limits the agents' scale and stable design and development.
“Furthermore, limited design, test, and deployment support for agent design, testing and deploy environments limits the agents' scale and stable design and development [135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Cooperative systems are highly susceptible to correlated system failures resulting in collusion even when there is no malicious intent, thereby diminishing fairness, robustness, and transparency.
“Cooperative systems are also highly susceptible to correlated system failures resulting in collusion-even when there is no malicious intent-thereby diminishing issues of fairness, robustness, and transparency [135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Data used by agentic AI may often be broken, incomplete, or poor in quality.
“Finally, when the data are used by agentic AI, they may often be broken, incomplete, or poor in quality, so this also leads to biased and unfair outcomes, and that may affect trust and fairness [135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Broken, incomplete, or poor-quality data used by agentic AI leads to biased and unfair outcomes.
“Finally, when the data are used by agentic AI, they may often be broken, incomplete, or poor in quality, so this also leads to biased and unfair outcomes, and that may affect trust and fairness [135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Human checks, backup systems, clear logs, and interpersonal support allow agentic AI systems to be more robust.
“Human checks, backup systems, clear logs, and interpersonal support allow them to be more robust [39,95,135].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [135]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01626-4 · Cited by 13,874 · see citing works
IOP Conference Series: Earth and Environmental Science
Hermansah, H., Sandi, N., & Naspendra, Z. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/issn.1755-1315 · Cited by 12,775 · see citing works
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S. E., et al. (2020) · doi:10.31222/osf.io/v7gm2_v1 · Cited by 8,090 · see citing works
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771 · see citing works
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117 · see citing works
Training Language Models to Follow Instructions with Human Feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117 · see citing works
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012 · see citing works
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Roberto Navigli, A., Neumaier, S., et al. (2021) · ACM Computing Surveys · doi:10.1145/3447772 · Cited by 2,371 · see citing works
Proceedings of the 13th International Conference on Neural Information Processing Systems
Leen, T. K., Dietterich, T. G., & Tresp, V. (2000) · Cited by 2,190 · see citing works
A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40231-1 · Cited by 1,941 · see citing works
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
Shneiderman, B. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1741118 · Cited by 1,743 · see citing works
A Categorical Framework of Manufacturing for Industry 4.0 and Beyond
Qin, J., Liu, Y., & Grosvenor, R. I. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.08.005 · Cited by 1,043 · see citing works
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.11805 · Cited by 990 · see citing works
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
Xiong, W., Hoang, T., & Wang, W. Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1060 · Cited by 862 · see citing works
Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717 · see citing works
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Gini, M. L., Shehory, O. M., Itō, T., & Jonker, C. M. (2013) · Cited by 703 · see citing works
INDUSTRY 4.0: A REVIEW ON INDUSTRIAL AUTOMATION AND ROBOTIC
Bahrin, M. A. K., Othman, M. F., Azli, N. H. N., & Talib, M. F. (2016) · Jurnal Teknologi · doi:10.11113/jt.v78.9285 · Cited by 680 · see citing works
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningPreprint
Rashid, T., Samvelyan, M., de Witt, C. S., Farquhar, G., Foerster, J., & Whiteson, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08839 · Cited by 545 · see citing works
Behavior Trees in Robotics and AI
Colledanchise, M., & Ögren, P. (2018) · CRC Press eBooks · doi:10.1201/9780429489105 · Cited by 534 · see citing works
Stabilising Experience Replay for Deep Multi-Agent Reinforcement LearningPreprint
Foerster, J., Nardelli, N., Farquhar, G., Afouras, T., Torr, P. H. S., Kohli, P., & Whiteson, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08887 · Cited by 487 · see citing works
Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019) · arXiv · doi:10.48550/arxiv.1904.12584 · Cited by 444 · see citing works
2018 IEEE Intelligent Vehicles Symposium (IV)
(2018) · doi:10.1109/iv43497.2018 · Cited by 420 · see citing works
A survey of Behavior Trees in robotics and AI
Iovino, M., Scukins, E., Styrud, J., Ögren, P., & Smith, C. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104096 · Cited by 316 · see citing works
2024 IEEE International Conference on Robotics and Automation (ICRA)
Mortimer, P., Hagmanns, R., Granero, M., Luettel, T., Petereit, J., & Wuensche, H.-J. (2024) · doi:10.1109/icra57147.2024 · Cited by 314 · see citing works
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022) · arXiv · doi:10.48550/arxiv.2201.07207 · Cited by 304 · see citing works
Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives
Martini, B., Bellisario, D., & Coletti, P. (2024) · Sustainability · doi:10.3390/su16135448 · Cited by 255 · see citing works
AI-chatbots on the services frontline addressing the challenges and opportunities of agency
Chong, T., Yu, T., Keeling, D. I., & de Ruyter, K. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102735 · Cited by 245 · see citing works
Meta Fundamental AI Research Diplomacy Team (FAIR)†, Bakhtin, A., Brown, N., Dinan, E., Farina, G., Flaherty, C., Fried, D., Goff, A., Gray, J., Hu, H., et al. (2022) · Science · doi:10.1126/science.ade9097 · Cited by 241 · see citing works
Hubmann, C., Becker, M., Althoff, D., Lenz, D., & Stiller, C. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995949 · Cited by 230 · see citing works
AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop
Li, C., Zheng, P., Li, S., Pang, Y. M., & Lee, C. K. M. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102321 · Cited by 223 · see citing works
Valladares, W., Galindo, M. A. G., Gutiérrez, J., Wu, W.-C., Liao, K.-K., Liao, J.-C., Lu, K.-C., & Wang, C. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.03.038 · Cited by 201 · see citing works
Virtual agents and flow experience: an empirical examination of AI-powered chatbots
Baabdullah, A. M., Alalwan, A. A., Algharabat, R. S., Metri, B., & Rana, N. P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121772 · Cited by 171 · see citing works
Cyber-Physical Systems in the Context of Industry 4.0: A Review, Categorization and Outlook
Oks, S. J., Jalowski, M., Lechner, M., Mirschberger, S., Merklein, M., Vogel‐Heuser, B., & Möslein, K. M. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10252-x · Cited by 169 · see citing works
Harms from Increasingly Agentic Algorithmic Systems
Chan, A., Salganik, R., Markelius, A., Pang, C., Rajkumar, N., Krasheninnikov, D., Langosco, L., He, Z., Duan, Y., Carroll, M., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594033 · Cited by 149 · see citing works
A Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2021) · Philosophical Studies Series · doi:10.1007/978-3-030-81907-1_2 · Cited by 134 · see citing works
Augmented Reality in Industry 4.0
Pace, F. D., Manuri, F., & Sanna, A. (2018) · American Journal of Computer Science and Information Technology · doi:10.21767/2349-3917.100017 · Cited by 134 · see citing works
A survey of Kubernetes scheduling algorithms
Senjab, K., Abbas, S., Ahmed, N., & Khan, A. U. R. (2023) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00471-1 · Cited by 134 · see citing works
Adel, A., & Saleem, N. H. (2024) · Smart Cities · doi:10.3390/smartcities7040068 · Cited by 112 · see citing works
Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109 · see citing works
On the Planning Abilities of Large Language Models - A Critical Investigation
Valmeekam, K., Marquez, M., Sreedharan, S., & Subbarao Kambhampati, B. (2023) · neural information processing systems · doi:10.52202/075280-3320 · Cited by 98 · see citing works
Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658948 · Cited by 84 · see citing works
From the origin of life to pandemics: emergent phenomena in complex systems
Artime, O., & Domenico, M. D. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0410 · Cited by 73 · see citing works
Zhang, S., Zhang, J., Chen, L., & Liu, X. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-019-05398-4 · Cited by 71 · see citing works
A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS
Chae, J., Lee, S., Jang, J., Hong, S., & Park, K. (2023) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2023.3323600 · Cited by 71 · see citing works
LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems
Kalyuzhnaya, A. V., Mityagin, S. A., Lutsenko, E., Getmanov, A., Aksenkin, Y., Fatkhiev, K., Fedorin, K., Nikitin, N. O., Chichkova, N., Vorona, V., et al. (2025) · Smart Cities · doi:10.3390/smartcities8010019 · Cited by 69 · see citing works
Choung, H., Seberger, J. S., & David, P. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2266244 · Cited by 67 · see citing works
Dancing on the inside: A qualitative study on online dance learning with teacher-AI cooperation
Kang, J., Kang, C., Yoon, J., Ji, H., Li, T., Moon, H., Ko, M., & Han, J. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11649-0 · Cited by 63 · see citing works
The power to harm: AI assistants pave the way to unethical behavior
Gratch, J. S., & Fast, N. J. (2022) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2022.101382 · Cited by 62 · see citing works
2023 ACM Conference on Fairness Accountability and Transparency
(2023) · doi:10.1145/3593013 · Cited by 61 · see citing works
AI and Ethics When Human Beings Collaborate With AI Agents
Cañas, J. J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.836650 · Cited by 60 · see citing works
The need to move away from agential-AI: Empirical investigations, useful concepts and open issues
Cabitza, F., Campagner, A., & Simone, C. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102696 · Cited by 55 · see citing works
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence
Manheim, D. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3020021 · Cited by 46 · see citing works
Design Foundations for AI Assisted Decision Making: A Self Determination Theory Approach
De Vreede, T., Raghavan, M., & De Vreede, G.-J. (2021) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.019 · Cited by 42 · see citing works
Managing the Twin Faces of AI: A Commentary on “Is AI Changing the World for Better or Worse?”
Shankar, V. (2024) · Journal of Macromarketing · doi:10.1177/02761467241286483 · Cited by 41 · see citing works
When Large Language Models Meet Optical Networks: Paving the Way for Automation
Wang, D., Wang, Y., Jiang, X., Zhang, Y., Pang, Y., & Zhang, M. (2024) · Electronics · doi:10.3390/electronics13132529 · Cited by 40 · see citing works
Jeon, Y. A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.028 · Cited by 39 · see citing works
Bias in perception of art produced by artificial intelligence
Hong, J.-W. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-91244-8_24 · Cited by 39 · see citing works
CRYSTAL: a multi-agent AI system for automated mapping of materials’ crystal structures
Gomes, C. P., Bai, J., Xue, Y., Björck, J., Rappazzo, B., Ament, S., Bernstein, R., Kong, S., Suram, S. K., Dover, R. B. V., et al. (2019) · MRS Communications · doi:10.1557/mrc.2019.50 · Cited by 37 · see citing works
Nam, K., Heo, S., Kim, S., & Yoo, C. (2023) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2023.103533 · Cited by 36 · see citing works
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
Zha, L., Cui, Y., Lin, L.-H., Kwon, M., Arenas, M. G., Zeng, A., Xia, F., & Sadigh, D. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610455 · Cited by 36 · see citing works
Cao, Y., Wang, R., Chen, M., & Barnawi, A. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2950730 · Cited by 35 · see citing works
Barriga, N. A., Stănescu, M., Besoaín, F., & Buro, M. (2019) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2019.2919363 · Cited by 31 · see citing works
Leveraging Conversational Systems to Assists New Hires During Onboarding
Chandar, P., Khazaeni, Y., Davis, M. J., Muller, M., Crasso, M., Liao, Q. V., Shami, N. S., & Geyer, W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67684-5_23 · Cited by 30 · see citing works
Deception and manipulation in generative AI
Tarsney, C. J. (2025) · Philosophical Studies · doi:10.1007/s11098-024-02259-8 · Cited by 30 · see citing works
Should Users Trust Advanced AI Assistants? Justified Trust As a Function of Competence and Alignment
Manzini, A., Keeling, G., Marchal, N., McKee, K. R., Rieser, V., & Gabriel, I. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658964 · Cited by 28 · see citing works
Towards an effective transnational regulation of AI
Gervais, D. J. (2021) · AI & Society · doi:10.1007/s00146-021-01310-0 · Cited by 28 · see citing works
ColorShapeLinks: A board game AI competition for educators and students
Fachada, N. (2021) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2021.100014 · Cited by 27 · see citing works
Artificial superintelligence and its limits: why AlphaZero cannot become a general agent
Jebari, K. A., & Lundborg, J. (2020) · AI & Society · doi:10.1007/s00146-020-01070-3 · Cited by 25 · see citing works
Navigating the Docker Ecosystem: A Comprehensive Taxonomy and Survey
Muzumdar, P., Bhosale, A., Basyal, G. P., & Kurian, G. (2024) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2024/v17i1411 · Cited by 22 · see citing works
Understanding human intention by connecting perception and action learning in artificial agents
Kim, S., Yu, Z., & Lee, M. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.01.009 · Cited by 21 · see citing works
2017 IEEE International Conference on Unmanned Systems (ICUS)
(2017) · doi:10.1109/icus42396.2017 · Cited by 21 · see citing works
Zhang, X., & Zhu, Q. (2023) · IEEE Network · doi:10.1109/mnet.005.2200408 · Cited by 21 · see citing works
Cloud Based Decision Making for Multi-agent Production Systems
Rehman, H. U., Pulikottil, T. B., Estrada-Jimenez, L. A., Mo, F., Chaplin, J. C., Barata, J., & Ratchev, S. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86230-5_53 · Cited by 20 · see citing works
Knowledge management system of an industry-specific research and education complex
Khabarov, V. I., & Volegzhanina, I. S. (2019) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/403/1/012197 · Cited by 19 · see citing works
Collective behavior of artificial intelligence population: transition from optimization to game
Zhang, S., Zhang, J., Huang, Z., Guo, B., Wu, Z.-X., & Wang, J. (2019) · Nonlinear Dynamics · doi:10.1007/s11071-018-4649-4 · Cited by 19 · see citing works
Investigation of the effects of nonverbal information on werewolf
Katagami, D., Takaku, S., Inaba, M., Ōsawa, H., Shinoda, K., Nishino, J., & Toriumi, F. (2014) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2014.6891847 · Cited by 18 · see citing works
Sharma, S., Devreaux, P., Sree, S. R., Scribner, D. R., Grynovicki, J. O., & Grazaitis, P. J. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.2.ervr-176 · Cited by 18 · see citing works
Psychological Perspectives on Intentional Forgetting: An Overview of Concepts and Literature
Ellwart, T., & Kluge, A. (2018) · Künstliche Intell. · doi:10.1007/s13218-018-00571-0 · Cited by 17 · see citing works
AMBY: A development environment for youth to create conversational agents
Tian, X., Kumar, A., Solomon, C. E., Calder, K. D., Katuka, G. A., Song, Y., Celepkolu, M., Pezzullo, L. G., Barrett, J. R., Boyer, K. E., et al. (2022) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2023.100618 · Cited by 17 · see citing works
Smart Fire Evacuation Service Based on Internet of Things Computing for Web3D
Yan, F., Jia, J., Hu, Y.-H., Guo, Q., & Zhu, H. (2019) · 網際網路技術學刊 · Cited by 17 · see citing works
Measuring the Non-Transitivity in Chess
Sanjaya, R. S., Wang, Y.-X., & Yang, Y. (2022) · Algorithms · doi:10.3390/a15050152 · Cited by 17 · see citing works
Kshetri, N. (2025) · Computer · doi:10.1109/mc.2025.3530304 · Cited by 16 · see citing works
A Deep Reinforcement Learning Blind AI in DareFightingICE
Nguyen, T. V., Dai, X., Khan, I., Thawonmas, R., & Pham, H. V. (2022) · IEEE Conference on Games (CoG) · doi:10.1109/cog51982.2022.9893718 · Cited by 16 · see citing works
Silva, D. D., Mills, N., Moraliyage, H., Rathnayaka, P., Wishart, S., & Jennings, A. (2025) · Smart Cities · doi:10.3390/smartcities8010034 · Cited by 15 · see citing works
Yager, K. G. (2024) · Digital Discovery · doi:10.1039/d4dd00178h · Cited by 12 · see citing works
Choung, H., Seberger, J. S., & David, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4356855 · Cited by 12 · see citing works
Srivastava, D. K., Lilly, J., & Feigh, K. (2024) · Cognition, Technology & Work · doi:10.1007/s10111-024-00771-9 · Cited by 11 · see citing works
Economics of Agentic AI in the Health-Care Industry
Kshetri, N. (2025) · IT Professional · doi:10.1109/mitp.2025.3529857 · Cited by 11 · see citing works
Coordination of modular nano grid energy management using multi-agent AI architecture
Renjith, P. N., Alfurhood, B. S., Prashanth, K. V., Patil, V. S., Sharma, N., & Chaturvedi, A. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109112 · Cited by 10 · see citing works
Lähdeaho, O., & Hilmola, O. (2023) · Supply Chain Analytics · doi:10.1016/j.sca.2023.100056 · Cited by 10 · see citing works
Fuzzy Ontology-based Intelligent Agent for High-School Student Learning in AI-FML Metaverse
Lee, C.-S., Wang, M., Huang, S. C., Yang, F.-J., Tsai, C.-H., & Wang, L.-Q. (2022) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee55066.2022.9882549 · Cited by 9 · see citing works
2022 IEEE Conference on Games (CoG)
(2022) · doi:10.1109/cog51982.2022 · Cited by 9 · see citing works
Multiagent AI implementations an emerging software engineering trend
Nourani, C. F. (1999) · Engineering Applications of Artificial Intelligence · doi:10.1016/s0952-1976(98)00042-6 · Cited by 8 · see citing works
Computer Models of Constitutive Social Practice
Evans, R. (2016) · Synthese Library/Synthese library · doi:10.1007/978-3-319-26485-1_23 · Cited by 8 · see citing works
Early Design of a Conversational AI Development Platform for Middle Schoolers
Kumar, A., Tian, X., Celepkolu, M., Israel, M., & Boyer, K. E. (2022) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc53370.2022.9833129 · Cited by 8 · see citing works
Leveraging Generative AI Tools for Proactive Risk Mitigation in Design
Hu, Y., A, A. N. N., Yellamati, D. D., & Goktas, Y. (2025) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/rams48127.2025.10935137 · Cited by 8 · see citing works
“Tipping the Balance”: Human Intervention in Large Language Model Multi‐Agent Debate
Triem, H., & Ding, Y. (2024) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1034 · Cited by 7 · see citing works
Zhang, Z., Kircher, K. J., Cai, Y., Brearley, J., Birge, D. P., & Norford, L. K. (2023) · Journal of Building Performance Simulation · doi:10.1080/19401493.2023.2180538 · Cited by 7 · see citing works
Bougzime, O., Jabbar, S., Cruz, C., & Demoly, F. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.11269 · Cited by 6 · see citing works
Shams, P., Beynier, A., Bouveret, S., & Nicolas Maudet, B. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13778 · Cited by 5 · see citing works
Tortorelli, A., Sabina, G., & Marchetti, B. (2024) · Energies · doi:10.3390/en17205199 · Cited by 5 · see citing works
Multi-Agent Artificial Intelligence in Pursuit Strategies: Breaking through the Stalemate
Franklin, D. M., & Markley, K. L. (2014) · The Florida AI Research Society · Cited by 4 · see citing works
Scaling AI Adoption in Finance: Modelling Framework and Implementation Study
Sepanosian, T., Milošević, Z., & Blair, A. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-79059-1_14 · Cited by 4 · see citing works
A Study of the Effectiveness of English Speaking of Teachable Agent using AI Chatbot
Lee, K. A., Lim, S., & Nagarajan, S. (2023) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0011730300003393 · Cited by 3 · see citing works
Roesler, E., Rieger, T., & Langer, M. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100116 · Cited by 3 · see citing works
Unlocking autism’s complexity: the Move Initiative’s path to comprehensive motor function analysis
Good, A. P., & Horn, E. (2025) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2024.1496165 · Cited by 3 · see citing works
Angelis, A., & Kousiouris, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5253384 · Cited by 3 · see citing works
AI inferences utilizing Occam Abduction
Crowder, J. A. (2016) · Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS) · doi:10.1109/nafips.2016.7851604 · Cited by 3 · see citing works
Innovative Intelligent Industrial Production and Logistics
Dassisti, M., Madani, K., & Panetto, H. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80775-6 · Cited by 3 · see citing works
Leveraging Large Language Models for Supply Chain Management Optimization: A Case Study
Rahman, S. A., Chawla, S., Yaqot, M., & Menezes, B. C. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80775-6_13 · Cited by 3 · see citing works
Towards trusting the ethical evolution of autonomous dynamic ecosystems
Cioroaica, E., Bühnová, B., & Tomur, E. (2022) · doi:10.1145/3526073.3527585 · Cited by 2 · see citing works
Can’t Bottom-up Artificial Moral Agents Make Moral Judgements?
Boyles, R. J. M. (2024) · Filosofija Sociologija · doi:10.6001/fil-soc.2024.35.1.3 · Cited by 2 · see citing works
AI Interfaced Learning Module for Road Safety using Virtual Reality
Sangeetha, V., Vamsidharan., V., Saran, R., & Shrikesh., S. P. (2022) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic53929.2022.9792882 · Cited by 2 · see citing works
Securing Agentic AI: Threats, Risks and Mitigation
Rashid, S. M. Z. U., Montasir, I., Haq, A., Ahmmed, M. T., & Alam, M. A. (2025) · doi:10.13140/rg.2.2.18420.67206 · Cited by 2 · see citing works
Results of the airlift challenge: a multi-agent AI planning competition
Delanovic, A., Chiu, C., Kolen, J. F., Gnanasekaran, A., Surana, A., Srivastava, K., Zhu, H., Lin, Y., Bukingolts, N., Willis, D., et al. (2023) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2664868 · Cited by 2 · see citing works
A Language Model's Guide Through Latent SpacePreprint
von Rütte, D., Anagnostidis, S., Bachmann, G., & Hofmann, T. F. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.14433 · Cited by 2 · see citing works
Marine unit offensive operation simulation based on multi-agent
Tiao-Ping, F., Jing, Q., & Jian-Hui, Z. (2017) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus.2017.8278382 · Cited by 1 · see citing works
Intuitive Searching: An Approach to Search the Decision Policy of a Blackjack Agent
Pan, Z., Xue, J., & Ge, T. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2380-6_77 · Cited by 1 · see citing works
Enterprise Design, Operations, and Computing. EDOC 2024 Workshops
Kaczmarek-Heß, M., Rosenthal, K., Suchánek, M., Silva, M. M. D., Proper, H. A., & Schnellmann, M. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-79059-1 · Cited by 1 · see citing works
Youvan, D. C. (2024) · doi:10.13140/rg.2.2.16899.52006 · Cited by 1 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
7 of the 144 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207 · see citing works
Patel, K., Shah, M., Qureshi, K. M., & Qureshi, M. R. N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11435-z · Cited by 32 · see citing works
Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems
Fernández‐Miguel, A., Ortíz-Marcos, S., Jiménez, M., del Hoyo, A. P. F., García‐Muiña, F. E., & Settembre‐Blundo, D. (2025) · Applied Sciences · doi:10.3390/app152111414 · Cited by 29 · see citing works
Kumar, N., Wei, X., & Zhang, H. (2026) · Information & Management · doi:10.1016/j.im.2026.104317 · Cited by 29 · see citing works
Goyez, J. P. L., González‐Briones, A., & Demazeau, Y. (2026) · Applied Sciences · doi:10.3390/app16031323 · Cited by 27 · see citing works
A Systematic Literature Review of Agentic AI: Definitions, Architectures, and Challenges
Moralles, C., Costa, L. A. L. F. D., Rigo, S. J., Kunst, R., Souza, V. C., Silva, E. P., Prado, G. L. E., Schardosim, T. D. C., & Roehrs, A. (2026) · IEEE Access · doi:10.1109/access.2026.3668138 · Cited by 16 · see citing works
Evaluating and regulating agentic AI: A study of benchmarks, metrics, and regulation
Farooq, A., Raza, S., Karim, N., Iqbal, H., Vasilakos, A. V., & Emmanouilidis, C. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104444 · Cited by 9 · see citing works
van Velzen, M., van der Willigen, R. F., de Beer, V. D., de Graaf-Waar, H. D., Janssen, E. R. C., van Leeuwen, S., van der Willigen, M. F., van der Willigen, M. J., Renardus, G., Maaroufi, R. E., et al. (2025) · Frontiers Artif. Intell. · doi:10.3389/frai.2025.1644084 · Cited by 11 · see citing works
Khan, A., Masood, F., Iqbal, A., Junaid, A., Arif, S., Al-Naeem, M., Husnain, G., & Alzahrani, A. S. (2026) · Scientific Reports · doi:10.1038/s41598-026-42158-y · Cited by 6 · see citing works
Agentic AI: A Survey of Autonomous Agents, Architectures, and Emerging Applications
Deshmukh, S., Kuri, M. M., Vanarote, V., Nimbalkar, M., Chandre, P. R., & Yadav, M. S. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323264 · Cited by 7 · see citing works
Toward Sustainable Agentic AI Systems: A Survey of Architectures and Methodologies
Lee, Y., & Park, E. (2026) · Sustainable Development · doi:10.1002/sd.70942 · Cited by 5 · see citing works
AI in control: Rethinking cybersecurity compliance and auditing
Loumachi, F. Y., Lacerda, M. J., Ouazzane, K., Adnane, A., & Adamyk, O. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108132 · Cited by 4 · see citing works
Artificial Intelligence agents and autonomous decision-making in business: a review
Ghane, M., Sorooshian, S., & Ang, M. C. (2026) · Cogent Business & Management · doi:10.1080/23311975.2026.2692205 · Cited by 4 · see citing works
Zhang, W., Wang, S., Zhang, S., Lei, Y., & Bao, L. (2025) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026825500154 · Cited by 4 · see citing works
Yang, Z., Wen, J., Bi, X., Lin, J., Zhao, Q., Zhang, P., & Zhang, L. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107142 · Cited by 4 · see citing works
Khapre, S., Mersha, M. A., Olalekan, Z., Shakil, H., Iskandar, S., Moustafa, A., & Kalita, J. K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134049 · Cited by 6 · see citing works
Rare-earth dopants for mechanoluminescence: from fundamentals to advanced applications
Ju, Z., Ansari, A. A., Wang, H., & Lv, R. (2026) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2026.218145 · Cited by 3 · see citing works
Agentic AI: A Perspective on Architecture, Frameworks and Applications
Raghavendra, P., & Saikia, M. J. (2026) · AI · doi:10.3390/ai7060219 · Cited by 4 · see citing works
Rafe, I. A., Dewan, M. R., & Islam, M. K. (2026) · European Journal of Applied Science Engineering and Technology · doi:10.59324/ejaset.2026.4(2).16 · Cited by 2 · see citing works
From Recommender to Actor: The Normative Boundary When RAG Tools Become Tool-Calling Agents
Rashid, M. T. (2026) · Minds and Machines · doi:10.1007/s11023-026-09782-z · Cited by 2 · see citing works
Syed, T. A., Siddiqui, M. S., Akarma, A., & Formisano, A. (2026) · Smart Cities · doi:10.3390/smartcities9070106 · Cited by 2 · see citing works
Sorooshian, S., Ahadi, N., & Liravi, P. (2026) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2026.101631 · Cited by 2 · see citing works
-, S. B., Ranjan, S., Adhitya, M., Suri, V., Hassija, V., Chamola, V., & Hussain, A. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10619-1 · Cited by 3 · see citing works
A New Paradigm of Metaverse Philosophy: From Anthropocentrism to Metasubjectivity
Kostenko, O. V., Dniprov, O., Zhuravlov, D., Тихомиров, О. О., & Ігорович, В. С. (2025) · Philosophies · doi:10.3390/philosophies10060117 · Cited by 1 · see citing works
Pan, Y., Du, Y., An, D., Ma, H. F., & Wu, S. (2025) · Infrared Physics & Technology · doi:10.1016/j.infrared.2025.106306 · Cited by 1 · see citing works
Jang, M. (2026) · Information · doi:10.3390/info17020212 · Cited by 2 · see citing works
ChatSUMO Agent: An LLM-Based Agent for Conversational Traffic Simulation in SUMO
Li, S., Ma, M., Azfar, T., & Ke, R. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105759 · Cited by 1 · see citing works
Liu, X., Pabolu, P., Jiang, Y., Fraser, T., Chen, R., & Gao, H. O. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133833 · Cited by 1 · see citing works
Márquez, A. C., & Fernández, J. F. G. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.112285 · Cited by 1 · see citing works
Shehada, F. M. F., & Mustafa, S. A. (2026) · Sustainability · doi:10.3390/su18179099 · Cited by 1 · see citing works
The Role of AI in Municipal Wastewater Treatment and Management: A Comprehensive Review
Otaru, A. J., Baba, I. A., Awe, O. B., Auta, M., Zaid, Z. A. A. A., & Al‐Yaari, M. (2026) · ChemBioEng Reviews · doi:10.1002/cben.70068 · Cited by 1 · see citing works
Vincent Gnanaraj, T., & Chenni Kumaran., J. (2025) · International Conference on Applying New Technology in Green Buildings (ATiGB) · doi:10.1109/atigb66719.2025.11142177
Rise of the Self-Driven Machine
Deshpande, A. A., Saraf, A. H., & Das, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7006-4.ch010
Autonomous Systems With State Self-Assessment and Functional Awareness Simulation
Majewski, M., Kacalak, W., Budniak, Z., & Czarnowski, I. (2026) · IEEE Access · doi:10.1109/access.2026.3656845 · Cited by 1 · see citing works
Designing Intelligent Agents for Real-Time Financial Risk Mitigation in ERP Environments
Beereddy, S. R., & Mane, V. M. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11378398
Chen, Y., Lin, L., Ruan, H., Chen, Y., Zhong, S., & Zu, L. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131905
Vincent Gnanaraj, T., & Amanullah, M. (2025) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit68829.2025.11434151
Interactive AI Training Agent for Tomato Farmers: A GenAI and LLM driven Approach to Agriculture 4.0
Gangadhar, S., & C., P. T. (2025) · International Conference on Emerging Computation and Information Technologies (ICECIT) · doi:10.1109/icecit67774.2025.11451172
Food industry 5.0: between evolution and revolution in food and agriculture
Hassoun, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36591-1.00010-0
Duy, P. T., Thanh, N. N., Lap, P. C., Ung, V.-G., Ngo, K.-K., Truong-Huu, T., & Pham, V.-H. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132546
An Impresiv AI–Driven Academic Data Assistant for Intelligent Academic Information Systems
Nababan, A. A., Dewananda, K. F., Zarqan, I. A., Pangaribuan, J. J., Irwansyah, Priyatna, R. D., Figna, H. P., & Jannah, M. (2026) · International Symposium on Computational and Business Intelligence (ISCBI) · doi:10.1109/iscbi69404.2026.11496267 · Cited by 1 · see citing works
Embodied AI in industrial operations: A systematic literature review
Koch, J., Prünte, P., Moenck, K., Determann, J., Gomse, M., & Schüppstuhl, T. (2026) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2026.103406
Toward Human-Aligned, Modular, and Emotion-Aware Multi-Agent Systems: A Systematic Literature ReviewPreprint
Ghanem, H., Cruz, C., & Cherifi, H. (2026) · Research Square · doi:10.21203/rs.3.rs-9485421/v1
Artificial Intelligence and Machine Learning in Industrial Maintenance Optimization
Fernández, J. F. G., & Márquez, A. C. (2026) · Engineering asset management review · doi:10.1007/978-3-032-21502-4_6
Naeem, S. M., Raza, S. H. M., Shabbir, A., & Hussaini, S. U. (2026) · International Conference on AI Innovations and Industry (ICAIII) · doi:10.1109/icaiii69475.2026.11521777
Explainable AI for Industrial Robotic Fault Diagnostics: Bridging the Trust Gap
Judijanto, L., & Nurhayati, S. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541352
PrivacyAssist: A User-Centric Agent Framework for Detecting Privacy Inconsistencies in Android Apps
Nguyen, T. T. L., Tullio, E. D., Carminati, B., & Ferrari, E. (2026) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3765613.3811680
Aasa, Z., Elias, F., Ekpo, S., Nwankwo, C., & Karimian, N. (2026) · Discover Electronics · doi:10.1007/s44291-026-00241-8
Agentic AI for the proactive identification of unforsseen disruptions in manufacturing supply chains
Nikolakis, N., Catti, P., & Alexopoulos, K. (2026) · International Conference on Integrated Intelligence and Cognitive Engineering (ICIICE) · doi:10.1109/iciice69672.2026.11565206
Ma, Q., Gan, M., Hou, X., & Zhang, X. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3697371
From Automation to Autonomy: The Role of Agentic AI in Industry 5.0
Srinivasu, P. N., Jose, A., Amer, A., Muda, Z. C., Madireddy, A., Chetana, V. L., Kumar, Y., & Ijaz, M. F. (2025) · Asian Conference on Communication and Networks (ASIANComNet) · doi:10.1109/asiancomnet68615.2025.11579681
Badawy, W. (2026) · ICEENG International Conference for Innovations in Intelligent Computing and Cybersecurity (IICC) · doi:10.1109/iicc69623.2026.11582739
An Agentic LLM Framework for Inventory Replenishment Planning in E-Commerce SMEs
Timtong, P., & Pannakkong, W. (2026) · Asia Pacific Information Technology Conference (APIT) · doi:10.1145/3796130.3796140
A Syntax and Orchestrator for Operationalising Composite AI Systems
Garcia-Perez, A., García-Echevarría, A., Miñón, R., Díaz-de-Arcaya, J., López-de-Armentia, J., Bonilla, L., & Zulueta, E. (2026) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet65726.2026.11633239
Heterogeneous Low-Power Agentic Architecture for Edge Distributed Home Surveillance
Zappia, L. L., Lattuada, M., Bianchessi, M. A., & Pau, D. P. (2026) · Electronics · doi:10.3390/electronics15143175
Multi-scale, multi-dimensional data mining for agentic AI micro-dramas recommendations
Liao, S.-H., Widowati, R., & Fang, Z.-C. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133755
“Who is Driving Anyway?” A Critical Survey of Trust and Confusion in “Symbiotic” Teleoperation
Haddad, S., Zouinar, M., Mualla, Y., & Abbas‐Turki, A. (2026) · International Conference on Human-AI Interaction and Experience Design (HAXD) · doi:10.1109/haxd70072.2026.11621021
Horváth, I. (2026) · Design and Artificial Intelligence · doi:10.1016/j.daai.2026.100097
Sakha, R., Venkat, S., Sekaran, R., Vaithiyanathan, R., Jeeva, S., & S, G. G. (2026) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs69077.2026.11634862
Transition from Dairy 4.0 to Dairy 5.0
Hassoun, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44730-3.00004-3
A Survey on Autonomous Compliance Enforcement using Agentic AI
Soudagar, S., Rajpurohit, V. S., Shahapurkar, A., & Sannakki, S. S. (2026) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss69635.2026.11646093
Autoda+: A Modular Agent-Based Framework for Integrated Data Analysis and Automated Machine Learning
Upadhyay, R., Singh, S., Theng, D. P., & Hiwale, M. (2026) · doi:10.1109/icetm68138.2026.11648100
Vásquez-Correa, J. L. (2026) · Frontiers in Human Dynamics · doi:10.3389/fhumd.2026.1904658
Kim, C., & Ahn, S. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14090212
HAMA: A Hierarchical Adaptive Multi-Agent Architecture for Industrial IoT Predictive Maintenance
Saleh, R., Dinh, K. P., Villányi, B. J., & Hy, T. S. (2026) · IEEE Access · doi:10.1109/access.2026.3731096
Security Challenges and Protection Strategies for Industrial AI Agents: A Comprehensive Survey
Gong, Y., Li, X., & Shi, J. (2026) · doi:10.1145/3838457.3838551
Khan, M. A., Pan, H., & Lin, Z. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.107274
Zabraoui, O., Hmamou, Y., Chafi, A., & Alami, S. K. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134471
Wang, M., Chen, L., Yin, M., Zhang, B., Wu, J., Sun, H., Yan, L., Wei, B., Zhang, F., Bi, C., et al. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1883295
Horváth, I., & Yu, C. (2026) · Design and Artificial Intelligence · doi:10.1016/j.daai.2026.100106
Xiao, J., Wang, Q., Zhou, H., Zhao, Y., & Lin, B. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129029
Singla, D., Singla, D., Anup, & Gupta, K. (2026) · International Conference on Computational Intelligence: Methods, Models, and Real-World Applications (ICCIMMR) · doi:10.1109/iccimmr71310.2026.11731721
Vision-Based Autonomous UAV Landing on Recessed Balcony in Multi-Story Buildings
Lee, G.-Y., Su, G.-H., & Liu, J.-S. (2026) · International Conference on Advanced Robotics and Intelligent Systems (ARIS) · doi:10.1109/aris70457.2026.11734489
An LLM-Based Agentic Pipeline for Generator-Level Adversarial Evaluation of Smart Grid IDSs
Quincozes, C. B., Mundt, G. C., Rankin, I., Quincozes, S. E., Severo, P. S., & Mossé, D. (2026) · Simpósio Brasileiro de Cibersegurança (SBSeg) · doi:10.5753/sbseg.2026.29283
Tang, Z., Ma, X., Liu, B., Qiu, H., Zhang, Z., Cao, G., & Guo, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.117233
- Authors
- Piccialli, F., Chiaro, D., Sarwar, S., Cerciello, D., Qi, P., & Mele, V.
- Affiliations
- University of Naples Federico II (IT)
- Corresponding author
- Piccialli, Francesco
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 291
- Pages
- 128404
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4410954314
- Cited by
- 77
- References
- 144
- Citations by year
- 2026: 63 · 2025: 14
- FWCI
- 37.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Expert Systems with Applications (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- autonomous agents, distributed artificial intelligence, Industry 4.0, Industry 5.0, Industry 6.0, multi-agent systems, decentralized systems, embodied AI, large language models, autonomous systems, human-AI collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
