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Journal articles on the topic 'Distributed artificial intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Pimple, Omkar, Umesh Saravane, and Neha Gavankar. "Cognitive Learning Using Distributed Artificial Intelligence." International Journal of Machine Learning and Computing 5, no. 1 (2015): 7–11. http://dx.doi.org/10.7763/ijmlc.2015.v5.474.

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2

Chaib-draa, B., R. Mandiau, and P. Millot. "Distributed artificial intelligence." ACM SIGART Bulletin 3, no. 3 (1992): 20–37. http://dx.doi.org/10.1145/140936.140937.

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3

Jagannathan, V., and Rajendra Dodhiawala. "Distributed artificial intelligence." ACM SIGART Bulletin, no. 95 (January 1986): 44–56. http://dx.doi.org/10.1145/1056563.1056571.

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4

Eduardo, Luis, and Castillo Hern. "On distributed artificial intelligence." Knowledge Engineering Review 3, no. 1 (1988): 21–57. http://dx.doi.org/10.1017/s0269888900004367.

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AbstractDistributed Artificial Intelligence has been loosely defined in terms of computation by distributed, intelligent agents. Although a variety of projects employing widely ranging methodologies have been reported, work in the field has matured enough to reveal some consensus about its main characteristics and principles. A number of prominent projects are described in detail, and two general frameworks, theSystem conceptual modeland theagent conceptual model, are used to compare the different approaches. The paper concludes by reviewing approaches to formalizing some of the more critical

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5

Klügl, Franziska. "Beyond Distributed Artificial Intelligence." KI - Künstliche Intelligenz 28, no. 3 (2014): 139–41. http://dx.doi.org/10.1007/s13218-014-0319-y.

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6

Dzitac, Ioan, and Boldur E. Bărbat. "Artificial Intelligence + Distributed Systems = Agents." International Journal of Computers Communications & Control 4, no. 1 (2009): 17. http://dx.doi.org/10.15837/ijccc.2009.1.2410.

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The connection with Wirth’s book goes beyond the title, albeit confining the area to modern Artificial Intelligence (AI). Whereas thirty years ago, to devise effective programs, it became necessary to enhance the classical algorithmic framework with approaches applied to limited and focused subdomains, in the context of broad-band technology and semantic web, applications - running in open, heterogeneous, dynamic and uncertain environments-current paradigms are not enough, because of the shift from programs to processes. Beside the structure as position paper, to give more weight to some basic

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7

Morse, David V. "Readings in distributed artificial intelligence." Artificial Intelligence in Engineering 6, no. 2 (1991): 103. http://dx.doi.org/10.1016/0954-1810(91)90005-9.

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8

Chaib-Draa, B., B. Moulin, R. Mandiau, and P. Millot. "Trends in distributed artificial intelligence." Artificial Intelligence Review 6, no. 1 (1992): 35–66. http://dx.doi.org/10.1007/bf00155579.

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9

Waters, Samuel T. "Readings in distributed artificial intelligence." Information Processing & Management 25, no. 5 (1989): 586. http://dx.doi.org/10.1016/0306-4573(89)90030-7.

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10

Eliëns, A. "Distributed Logic Programming for Artificial Intelligence." AI Communications 4, no. 1 (1991): 11–21. http://dx.doi.org/10.3233/aic-1991-4103.

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11

Molinara, M., A. Bria, S. De Vito, and C. Marrocco. "Artificial intelligence for distributed smart systems." Pattern Recognition Letters 142 (February 2021): 48–50. http://dx.doi.org/10.1016/j.patrec.2020.12.006.

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12

Reuter, Lukas, Jan Ole Berndt, Anna-Sophie Ulfert, Conny H. Antoni, Thomas Ellwart, and Ingo J. Timm. "Intentional Forgetting in Distributed Artificial Intelligence." KI - Künstliche Intelligenz 33, no. 1 (2018): 69–77. http://dx.doi.org/10.1007/s13218-018-0566-4.

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13

Montes, Gabriel Axel, and Ben Goertzel. "Distributed, decentralized, and democratized artificial intelligence." Technological Forecasting and Social Change 141 (April 2019): 354–58. http://dx.doi.org/10.1016/j.techfore.2018.11.010.

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14

Rosenman, Michael A. "Distributed artificial intelligence: Theory and praxis." Knowledge-Based Systems 7, no. 2 (1994): 147–48. http://dx.doi.org/10.1016/0950-7051(94)90028-0.

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15

Chu, Eric, K. Srihari, and C. R. Emerson. "Distributed artificial intelligence in process control." Computers & Industrial Engineering 31, no. 1-2 (1996): 397–400. http://dx.doi.org/10.1016/0360-8352(96)00160-x.

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16

Balador, Ali, Sima Sinaei, Mats Pettersson, and Ilhan Kaya. "DAIS Project - Distributed Artificial Intelligence Systems." ACM SIGAda Ada Letters 42, no. 2 (2023): 96–98. http://dx.doi.org/10.1145/3591335.3591348.

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DAIS is a step forward in the area of artificial intelligence and edge computing. DAIS intends to create a complete framework for self-organizing, energy efficient and private-by-design distributed AI. DAIS is a European project with a consortium of 47 partners from 11 countries coordinated by RISE Research Institute of Sweden.

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17

Shih, Wurong, and K. Srihari. "Distributed Artificial Intelligence in manufacturing systems control." Computers & Industrial Engineering 29, no. 1-4 (1995): 199–203. http://dx.doi.org/10.1016/0360-8352(95)00071-8.

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18

Shaw, Michael J., and Mark S. Fox. "Distributed artificial intelligence for group decision support." Decision Support Systems 9, no. 4 (1993): 349–67. http://dx.doi.org/10.1016/0167-9236(93)90046-6.

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19

Seng, Kah Phooi, Li Minn Ang, and Ericmoore Ngharamike. "Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks." International Journal of Distributed Sensor Networks 18, no. 3 (2022): 155014772110628. http://dx.doi.org/10.1177/15501477211062835.

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The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artific

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20

Danhof, Kenneth J., and Mehdi Zargham. "Toward a paradigm for distributed intelligence." Applied Intelligence 1, no. 4 (1992): 335–43. http://dx.doi.org/10.1007/bf00122021.

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21

Kol’chugina, E. A. "Self-organizing software systems with distributed artificial intelligence." Automatic Control and Computer Sciences 49, no. 4 (2015): 216–20. http://dx.doi.org/10.3103/s0146411615040069.

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22

Corchado, Juan M., Li Weigang, Javier Bajo, Fei Wu, and Tian-cheng Li. "Special issue on distributed computing and artificial intelligence." Frontiers of Information Technology & Electronic Engineering 17, no. 4 (2016): 281–82. http://dx.doi.org/10.1631/fitee.dcai2015.

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23

Kiountouzis, Evangelos, and Christos Papatheodorou. "Distributed Artificial Intelligence and Soft Systems: A Comparison." Journal of the Operational Research Society 41, no. 5 (1990): 441. http://dx.doi.org/10.2307/2583376.

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24

Carlson, Kristen W. "Safe Artificial General Intelligence via Distributed Ledger Technology." Big Data and Cognitive Computing 3, no. 3 (2019): 40. http://dx.doi.org/10.3390/bdcc3030040.

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Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. A set of logically distinct conceptual components is proposed that are necessary and sufficient to (1) ensure various AGI scenarios will not harm humanity, and (2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, “blockchain”) is integral to this proposal, e.g., “smart contr

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25

Bond, A. H., and L. Gasser. "A subject-indexed bibliography of distributed artificial intelligence." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 6 (1992): 1260–81. http://dx.doi.org/10.1109/21.199455.

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26

Kiountouzis, Evangelos, and Christos Papatheodorou. "Distributed Artificial Intelligence and Soft Systems: A Comparison." Journal of the Operational Research Society 41, no. 5 (1990): 441–46. http://dx.doi.org/10.1057/jors.1990.69.

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27

Bajo, Javier, and Juan M. Corchado. "Neural networks in distributed computing and artificial intelligence." Neurocomputing 272 (January 2018): 1–2. http://dx.doi.org/10.1016/j.neucom.2017.06.022.

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28

Borda, J., R. González, and M. Insunza. "Artificial Intelligence Techniques on a Distributed Control Environment." IFAC Proceedings Volumes 23, no. 3 (1990): 45–49. http://dx.doi.org/10.1016/s1474-6670(17)52532-x.

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29

Bajo, Javier, and Juan M. Corchado. "Neural Systems in Distributed Computing and Artificial Intelligence." Neurocomputing 231 (March 2017): 1–2. http://dx.doi.org/10.1016/j.neucom.2016.08.096.

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30

Findler, N. V., and R. Lo. "Distributed artificial intelligence approach to air traffic control." IEE Proceedings D Control Theory and Applications 138, no. 6 (1991): 515. http://dx.doi.org/10.1049/ip-d.1991.0072.

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31

Velthuijsen, H. "Distributed artificial intelligence for runtime feature-interaction resolution." Computer 26, no. 8 (1993): 48–55. http://dx.doi.org/10.1109/2.223543.

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32

Motus, L. "Distributed artificial intelligence series: Research notes in AI." Engineering Applications of Artificial Intelligence 1, no. 2 (1988): 143–44. http://dx.doi.org/10.1016/0952-1976(88)90039-5.

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33

Hewitt, Carl. "Open Information Systems Semantics for distributed artificial intelligence." Artificial Intelligence 47, no. 1-3 (1991): 79–106. http://dx.doi.org/10.1016/0004-3702(91)90051-k.

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34

Adorni, Giovanni, and Agostino Poggi. "An object-oriented language for distributed artificial intelligence." International Journal of Man-Machine Studies 38, no. 3 (1993): 435–53. http://dx.doi.org/10.1006/imms.1993.1020.

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35

Guleva, Valentina, Egor Shikov, Klavdiya Bochenina, Sergey Kovalchuk, Alexander Alodjants, and Alexander Boukhanovsky. "Emerging Complexity in Distributed Intelligent Systems." Entropy 22, no. 12 (2020): 1437. http://dx.doi.org/10.3390/e22121437.

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Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and it

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36

Lv, Peng Liang, and Guo Shun Chen. "Distributed Remote Monitoring System Key Technology Research on Agent." Advanced Materials Research 860-863 (December 2013): 2774–82. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2774.

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Recent years, as computer science and the development of high-tech communications networks, distributed artificial intelligence field of artificial intelligence research as an important branch, more and more industry attention. One of the Agent and Multi-Agent technology research became a hot topic of distributed artificial intelligence research. Research on Multi-Agent technology is mainly a group of autonomous distributed open Agent in a dynamic environment, through interaction, cooperation, competition, negotiation and other acts perform complex control or task solving. Because of its bette

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37

Fransman, Jeroen, Joris Sijs, Henry Dol, Erik Theunissen, and Bart De Schutter. "Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver." Journal of Artificial Intelligence Research 76 (January 23, 2023): 393–433. http://dx.doi.org/10.1613/jair.1.14151.

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In this paper, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the Continuous Distributed Constraint Optimization Problem (C-DCOP) framework. This framework extends the classical DCOP framework towards utility functions with continuous domains. D-Bay solves a C-DCOP by utilizing Bayesian optimization for the adaptive sampling of variables. We theoretically show that D-Bay converges to the global optimum of the C-DCOP for Lipschitz continuous utility functions. The performance of the algorithm is evaluated empirically based on the sample eff

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38

Kalyan Chakravarthy Thatikonda. "Distributed Intelligence for Distributed Systems Resilience: A Meta-Analysis of Artificial Intelligence Driven Self-Healing Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1123–34. https://doi.org/10.32628/cseit25112448.

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This paper presents a meta-analysis of artificial intelligence applications for autonomous self-healing in distributed systems during cloud infrastructure failures. Previous research has established that distributed systems experience availability zone outages and network partitions with increasing frequency as system complexity grows (Chen et al., 2022; Gunawi et al., 2023). While traditional resilience approaches have focused on redundancy and manual recovery procedures (Zhang, 2021; Verma et al., 2024), they frequently fail to address the inherent uncertainty in complex failure propagation

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39

Ma, You Jie, Fan Ting Kong, and Xue Song Zhou. "Summary on Research of Agent and Multi-Agent System." Advanced Materials Research 542-543 (June 2012): 1380–83. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1380.

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Distributed artificial intelligence is an important branch of the artificial intelligence， Agent and Multi-agent system are important aspects of distributed artificial intelligence research. This paper mainly introduce some research and exiting problems about Agent and Multi-agent system in recent years, including the Agent concept, characteristics and structure, Multi-agent system concept, structure and the coordination problem. Finally, I look forward to the development trend of Agent and Multi-agent system.

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40

Wang, Y., C. Rizos, L. Ge, M. Tanner, and M. Dwyer. "Distributed artificial intelligence in GIS ‐ Geolet: A distributed geo‐spatial information retriever." Journal of Spatial Science 50, no. 2 (2005): 37–50. http://dx.doi.org/10.1080/14498596.2005.9635048.

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41

Yeoh, William, and Makoto Yokoo. "Distributed Problem Solving." AI Magazine 33, no. 3 (2012): 53. http://dx.doi.org/10.1609/aimag.v33i3.2429.

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Distributed problem solving is a subfield within multiagent systems, where agents are assumed to be part of a team and collaborate with each other to reach a common goal. In this article, we illustrate the motivations for distributed problem solving and provide an overview of two distributed problem solving models, namely distributed constraint satisfaction problems (DCSPs) and distributed constraint optimization problems (DCOPs), and some of their algorithms.

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42

Craig, Ian. "Distributed Artificial Intelligence edited by Michael N. Huhns Research Notes in Artificial Intelligence, Pitman, London, 1987." Robotica 6, no. 2 (1988): 171. http://dx.doi.org/10.1017/s0263574700004227.

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43

Alhiti, Hasim Abdul Rahman. "Artificial Intelligence Enhances Heart Surgeries." South East European Journal of Cardiology 4, no. 1 (2023): 67–69. http://dx.doi.org/10.3889/seejca.2023.6056.

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BACKGROUND: Heart surgery is used to treat serious heart diseases. METHODS: This is a perspective article on heart surgeries, by reviewing the papers on Europe percutaneous mitral commissurotomy (PMC) concerning heart surgeries, as Europe PMC is a trusted academic engine. The author included all papers on the subject, but papers from the last 5 years are the favorite for references, without exclusion from review. RESULTS: There are 192,265 case reports (28.5%), 143,267 clinical trials (21.2%), 303,690 reviews (45%), and 34,323 miscellaneous (5%). CONCLUSIONS: Artificial intelligence is the fut

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44

Arévalo, Paul, and Francisco Jurado. "Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids." Energies 17, no. 17 (2024): 4501. http://dx.doi.org/10.3390/en17174501.

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This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the lates

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45

Mikhailov, Igor. "Natural Computations and Artificial Intelligence." Chelovek 33, no. 2 (2022): 65. http://dx.doi.org/10.31857/s023620070019511-9.

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The research program focused on the analysis of computational approaches to natural and artificial intelligence is one of four accepted for implementation at the Center for the Philosophy of Consciousness and Cognitive Sciences of the Institute of Philosophy, Russian Academy of Sciences. Presumably, it should become a direction of interdisciplinary research at the crossroad of philosophy, cognitive psychology, cognitive and social neuroscience, and artificial intelligence. The working hypothesis proposed for discussion attended by the relevant specialists is as follows: if an acceptable comput

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46

Rivera-Escobedo, Manuel, Manuel de Jesús López-Martínez, Luis Octavio Solis-Sánchez, et al. "Low-Scalability Distributed Systems for Artificial Intelligence: A Comparative Study of Distributed Deep Learning Frameworks for Image Classification." Applied Sciences 15, no. 11 (2025): 6251. https://doi.org/10.3390/app15116251.

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Artificial intelligence has experienced tremendous growth in various areas of knowledge, especially in computer science. Distributed computing has become necessary for storing, processing, and generating large amounts of information essential for training artificial intelligence models and algorithms that allow knowledge to be created from large amounts of data. Currently, cloud services offer products for running distributed data training, such as NVIDIA Deep Learning Solutions, Amazon SageMaker, Microsoft Azure, and Google Cloud AI Platform. These services have a cost that adapts to the need

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47

Fan, Tao. "Image Recognition and Simulation Based on Distributed Artificial Intelligence." Complexity 2021 (April 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/5575883.

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This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients (HOG) feature extraction and Support Vector Machine (SVM) classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis (PCA) to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition exp

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48

Radwan, Muhammad, Amr Badr, and Ibrahim Farag. "Task Allocation in Distributed Artificial Intelligence using Boids Model." International Journal of Computer Applications 53, no. 2 (2012): 40–46. http://dx.doi.org/10.5120/8397-2028.

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49

M-El-Emary, Ibrahiem M. "Managing a High Speed LAN using Distributed Artificial Intelligence." Journal of Computer Science 2, no. 1 (2006): 92–96. http://dx.doi.org/10.3844/jcssp.2006.92.96.

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50

Glukhovskii, Andrei S. "Distributed Moral Responsibility in the Field of Artificial Intelligence." Ethical Thought 24, no. 1 (2024): 129–43. http://dx.doi.org/10.21146/2074-4870-2024-24-1-129-143.

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The main goal of the article is to justify the optimality of Luciano Floridi’s concept of distributed morality for describing a model of the relationship of responsibility in complex human – AI systems. The high level of its autonomy in functioning does not correspond to the classical instrumentalist approach to technology, as it turns out to be capable of fundamentally uncontrollable actions in the process of performing the task assigned to it. For the same reason, the classical definition of moral responsibility does not adequately correspond to the actual situation in the field of artif

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