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Journal articles on the topic 'Distributed artificial intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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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 (February 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 (August 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 (March 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 capabilities required by multi-agent interaction.

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5

Klügl, Franziska. "Beyond Distributed Artificial Intelligence." KI - Künstliche Intelligenz 28, no. 3 (July 18, 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 (March 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 assertions, results of recent research are abridged and commented upon in line with new paradigms. Among the conclusions: a) Nondeterministic software is unavoidable; its development entails not just new design principles but new computing paradigms. b) Agent-oriented systems, to be effectual, should merge conventional agent design with approaches employed in advanced distributed systems (where parallelism is intrinsic to the problem, not just a mean to speed up).

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7

Morse, David V. "Readings in distributed artificial intelligence." Artificial Intelligence in Engineering 6, no. 2 (April 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 (January 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 (December 3, 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 (June 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 (October 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 (April 5, 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 (September 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 (June 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 (March 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 artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things.

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20

Danhof, Kenneth J., and Mehdi Zargham. "Toward a paradigm for distributed intelligence." Applied Intelligence 1, no. 4 (May 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 (July 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 (April 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 (May 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 (July 8, 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 contracts” are necessary to address the evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: (1) Access to technology by market license. (2) Transparent ethics embodied in DLT. (3) Morality encrypted via DLT. (4) Behavior control structure with values at roots. (5) Individual bar-code identification of critical components. (6) Configuration Item (from business continuity/disaster recovery planning). (7) Identity verification secured via DLT. (8) “Smart” automated contracts based on DLT. (9) Decentralized applications—AI software modules encrypted via DLT. (10) Audit trail of component usage stored via DLT. (11) Social ostracism (denial of resources) augmented by DLT petitions. (12) Game theory and mechanism design.

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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 (May 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 (September 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 (August 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 (June 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 (January 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 (March 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 (December 19, 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 its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

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36

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 efficiency. The proposed algorithm is compared to state-of-the-art DCOP and C-DCOP solvers. The algorithm generates better solutions while requiring fewer samples.

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37

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 better reflect human social intelligence, more suitable for an open, dynamic social environment, thus causing researchers in various fields of interest, and is widely used in scientific computing, computer network e-commerce, business management and traffic control, etc

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38

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 (December 2005): 37–50. http://dx.doi.org/10.1080/14498596.2005.9635048.

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39

Alhiti, Hasim Abdul Rahman. "Artificial Intelligence Enhances Heart Surgeries." South East European Journal of Cardiology 4, no. 1 (November 23, 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 future of advances in heart surgeries as they are highly distributed in the world.

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40

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

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41

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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42

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 computational theory of mind appears, we will be able to restrict our research to a simple scientific ontology describing only parts of a physical implementation of computational algorithms, adding a relevant version of computational mathematics thereto. Another hypothesis proposed is that there is an essential ontological intersection between the mechanisms underlying human cognitive abilities and their social organization, both of which serving as an implementation medium for complex distributed cognitive computations. Particularly those which are associated with social organization are responsible for logical and verbal (“rational”) cognitive abilities. As a result of some previous research, an ontology of nested distributed computational systems was generally formulated, which, as expected, can demonstrate significant heuristic potential if supplemented with an adequate mathematical apparatus. Since only individuals with certain cognitive abilities can be social agents, a philosophical problem arises: are cognitive abilities necessary or sufficient to involve their carriers in stable social interactions? In the first case, we have a weak thesis about the cognitive determination of sociality, in the second — the strong one. The choice between these positions is, too, a subject of future research.

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43

Yeoh, William, and Makoto Yokoo. "Distributed Problem Solving." AI Magazine 33, no. 3 (September 20, 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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44

Kumar, Kamal, Vinod Kumar, Seema, Mukesh Kumar Sharma, Akber Ali Khan, and M. Javed Idrisi. "A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems." Journal of Computer Networks and Communications 2024 (February 9, 2024): 1–15. http://dx.doi.org/10.1155/2024/9979371.

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Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.

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45

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 experiments, the results show that the image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit (GPU) is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.

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46

Radwan, Muhammad, Amr Badr, and Ibrahim Farag. "Task Allocation in Distributed Artificial Intelligence using Boids Model." International Journal of Computer Applications 53, no. 2 (September 25, 2012): 40–46. http://dx.doi.org/10.5120/8397-2028.

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47

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

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48

Trannois, H. "Microscopic simulation of an intersection using Distributed Artificial Intelligence." Recherche - Transports - Sécurité 59 (June 1998): 33–42. http://dx.doi.org/10.1016/s0761-8980(98)80003-4.

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49

Bajo, Javier, and Juan M. Corchado. "Special issue on distributed computing and artificial intelligence systems." Neurocomputing 172 (January 2016): 382–84. http://dx.doi.org/10.1016/j.neucom.2015.05.114.

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50

Sim, Siang Kok. "The distributed artificial intelligence approach to flexible manufacturing system." Journal of Mechanical Working Technology 17 (August 1988): 489–98. http://dx.doi.org/10.1016/0378-3804(88)90049-6.

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