Relevant bibliographies by topics / Distributed artificial intelligence

Contents

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

Academic literature on the topic 'Distributed artificial intelligence'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Distributed artificial intelligence"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Distributed artificial intelligence"

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Sian, Satwinder Singh. "Learning in distributed artificial intelligence systems." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282757.

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Rampal, Gaurav S. "Developing mobile distributed intelligent network services using RM-ODP." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ32556.pdf.

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Wei, Jin. "Modeling and Coordination of interconnected microgrids using distributed artificial intelligence approaches." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA021.

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À mesure que les sources renouvelables pénètrent dans le système électrique actuel pour atténuer le réchauffement planétaire et la pénurie d’énergie, le concept de microréseau (MG) permet de réduire l’impact de la production intermittente sur le réseau de services publics. Il permet d'améliorer l’automatisation et l’intelligence du réseau électrique avec des caractéristiques plug-and-play. L’intégration d’un plus grand nombre de MG dans un réseau de distribution favorise le développement du réseau intelligent. Leur coordination pourrait conduire à une grande fiabilité du système avec un faible coût, et une forte résistance aux pannes électriques. La réalisation de ces profits repose sur des technologies développées de communication et de stratégies de contrôle.La répartition de la puissance dans les MG répartis tout en coordonnant les éléments au sein de chaque MG exige un contrôle décentralisé. L'approche multiagent permet de modéliser un réseau de MG comme un système physiquement distribué. Cette thèse étudie principalement le contrôle de coordination dans le réseau MG et sa modélisation à base d'agent.. L' objectif est de promouvoir la performance des contrôles en termes d’efficacité et de fiabilité. Deux méthodes sont envisagées pour permettre l’évolutivité du système, y compris la coordination avec les MG voisins et dans la zone de coordination étendue. Une plateforme de simulation est établie pour valider les approches proposées.Les stratégies de contrôle pour la coordination entre les MG et leurs voisins sont proposées afin de maintenir la charge complète et la sécurité tout en minimisant le coût de production. Le contrôle centralisé dans le groupe de coordination est appliqué à la gestion économique de l’énergie. Il utilise une méthode de Newton-Raphson pour répartir la puissance entre les MG voisins en simplifiant la relation entre le coût de production de MG et sa puissance de sortie. Une approche fondée sur le consensus est adoptée pour calculer le flux de puissance du réseau, et les résultats sont comparés avec la capacité maximale sur la ligne pour assurer un fonctionnement sûr. Pour améliorer encore les avantages économiques, l’approximation de la relation entre la puissance de production de MG et le coût de production est améliorée par une autre stratégie fondée sur la notion de marché. Il construit un marché pour le commerce d’électricité avec les voisins. Cette méthode préserve la vie privée de chaque MG. Le calcul du débit de puissance est simplifié pour être proportionnel à la différence d’angle entre les deux extrémités de la ligne de raccordement. Les deux stratégies sont testées sur plusieurs réseaux MG. Leur performance montre que les deux approches sont évolutives et pourraient économiquement compenser le manque d’approvisionnement en charge dans les MG défectueux.Pour la stratégie de contrôle avec une fiabilité et un profit plus élevés, une stratégie de coordination au sein d’une vaste zone sélectionnée de MG est proposée. L’élargissement de la zone de coordination en fonction des MG voisins fournit plus de sources d’énergie au MG. Il assure suffisamment de puissance pour compenser le déséquilibre et offre plus de choix pour la distribution de puissance. La sélection de la zone de coordination est réalisée par un algorithme évolutionnaire distribué. La programmation quadratique dans Gurobi est utilisée pour résoudre le problème de répartition de puissance. Un autre algorithme génétique est également adopté pour résoudre le problème de la répartition optimale de la puissance avec un coût de production quadratique pour la microturbine. La performance de cette stratégie est testée, et les résultats montrent qu’elle a des avantages en termes de fiabilité, d'évolutivité et de profit par rapport aux méthodes centralisées
As renewable sources penetrate the current electrical system to relief global warming and energy shortage, microgrid (MG) emerges to reduce the impact of intermittent generation on the utility grid. Additionally, it improves the automation and intelligence of the power grid with plug-and-play characteristics. Inserting more MGs into a distribution network promotes the development of the smart grid. Thus MG networks existing in the power system are in prospect. Coordinating them could gain a system with high reliability, low cost, and strong resistance to electrical faults. Achieving these profits relies on developed technologies of communication, control strategy, and corresponding algorithms.Dispatching power in distributed MGs while coordinating elements within the individual MG demands a decentralized control system, in which the multi-agent system possesses advantages. It is applied to the MG network for establishing a physically distributed system. Based on the multi-agent system, this thesis mainly studies the coordination control in the MG network and its modeling. It aims at promoting control performance in terms of efficiency, reliability, economic benefit, and scalability. Two methods are considered to enable the system scalability, including the coordination with neighboring MGs and within the extensive coordinating area. A simulation platform is established to validate the proposed approaches.The control strategies for coordination between MGs and their neighbors are proposed to maintain the complete load supply and global security operation while minimizing the generation cost. Centralized control in the coordination group is applied for economic energy management. It uses a Newton-Raphson method to dispatch power among neighboring MGs by simplifying the relationship between MG generation cost and its output power. An average consensus approach is adopted to calculate the caused network power flow, and the results are compared with the maximal capacity on the line to keep safe operation. To further improve the economic benefits, the approximation of the relationship between MG output power and the caused generation cost is improved by an another strategy based on the market concept. It builds a market for neighboring power trade. This method maintains the operation privacy of individual MG. Power flow calculation is simplified to be proportional to the angle difference between the two terminates of the connecting line. Both strategies are tested on several MG network. Their performance shows that both approaches possess scalability and could economically compensate for the lack of load supply in faulted MG.For the control strategy with higher reliability and profit, a coordination strategy within a selected extensive area of MGs is proposed. Expanding the coordination area based on neighboring MGs provides more energy sources to the demanded MG. It ensures enough power to compensate imbalance and offers more choices for power dispatching. The selection of the coordination area is based on a distributed evolutionary algorithm. Quadratic programming in Gurobi is used to solve the power dispatching problem. Another genetic algorithm is also adopted to solve the problem of optimal power dispatching with a quadratic generation cost for microturbine. The performance of this strategy is tested, and the results show that it has comprehensive advantages on reliability, scalability, and profit compared with centralized methods
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Lucouw, Alexander. "Distributed fault detection and diagnostics using artificial intelligence techniques / A. Lucouw." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4110.

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With the advancement of automated control systems in the past few years, the focus has also been moved to safer, more reliable systems with less harmful effects on the environment. With increased job mobility, less experienced operators could cause more damage by incorrect identification and handling of plant faults, often causing faults to progress to failures. The development of an automated fault detection and diagnostic system can reduce the number of failures by assisting the operator in making correct decisions. By providing information such as fault type, fault severity, fault location and cause of the fault, it is possible to do scheduled maintenance of small faults rather than unscheduled maintenance of large faults. Different fault detection and diagnostic systems have been researched and the best system chosen for implementation as a distributed fault detection and diagnostic architecture. The aim of the research is to develop a distributed fault detection and diagnostic system. Smaller building blocks are used instead of a single system that attempts to detect and diagnose all the faults in the plant. The phases that the research follows includes an in-depth literature study followed by the creation of a simplified fault detection and diagnostic system. When all the aspects concerning the simple model are identified and addressed, an advanced fault detection and diagnostic system is created followed by an implementation of the fault detection and diagnostic system on a physical system.
Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.
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Li, Xiao-Dong Carleton University Dissertation Engineering Electrical. "DISC: a framework for coordination in a distributed artificial intelligence system." Ottawa, 1992.

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Balamuru, Vinay Gopal. "The Role of Intelligent Mobile Agents in Network Management and Routing." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2736/.

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In this research, the application of intelligent mobile agents to the management of distributed network environments is investigated. Intelligent mobile agents are programs which can move about network systems in a deterministic manner in carrying their execution state. These agents can be considered an application of distributed artificial intelligence where the (usually small) agent code is moved to the data and executed locally. The mobile agent paradigm offers potential advantages over many conventional mechanisms which move (often large) data to the code, thereby wasting available network bandwidth. The performance of agents in network routing and knowledge acquisition has been investigated and simulated. A working mobile agent system has also been designed and implemented in JDK 1.2.
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Wong, Wing-ki Vicky. "An immunity-based distributed multiagent control framework." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37314348.

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Ling, Nai Man. "The application of artificial intelligence and distributed control to a robotic workcell /." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=55617.

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Wallace, Andrew Alexander. "Application of distributed artificial intelligence to the flow control of mobile robots." Thesis, Open University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395248.

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Khoui, Hooman. "Investigations into distributed artificial intelligence techniques for design with applications to instruments." Thesis, City University London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283157.

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Books on the topic "Distributed artificial intelligence"

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Chen, Jie, Jérôme Lang, Christopher Amato, and Dengji Zhao, eds. Distributed Artificial Intelligence. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94662-3.

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Yadav, Satya Prakash, Dharmendra Prasad Mahato, and Nguyen Thi Dieu Linh. Distributed Artificial Intelligence. Edited by Satya Prakash Yadav, Dharmendra Prasad Mahato, and Nguyen Thi Dieu Linh. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467.

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Taylor, Matthew E., Yang Yu, Edith Elkind, and Yang Gao, eds. Distributed Artificial Intelligence. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64096-5.

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N, Huhns Michael, ed. Distributed artificial intelligence. London: Pitman, 1987.

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Yokoo, Makoto, Hong Qiao, Yevgeniy Vorobeychik, and Jianye Hao, eds. Distributed Artificial Intelligence. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25549-6.

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de Leon F. de Carvalho, Andre Ponce, Sara Rodríguez-González, Juan F. De Paz Santana, and Juan M. Corchado Rodríguez, eds. Distributed Computing and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14883-5.

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Omatu, Sigeru, José Neves, Juan M. Corchado Rodriguez, Juan F. Paz Santana, and Sara Rodríguez Gonzalez, eds. Distributed Computing and Artificial Intelligence. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00551-5.

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Omatu, Sigeru, Juan F. De Paz Santana, Sara Rodríguez González, Jose M. Molina, Ana M. Bernardos, and Juan M. Corchado Rodríguez, eds. Distributed Computing and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28765-7.

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H, Bond Alan, and Gasser Leslie George 1949-, eds. Readings in distributed artificial intelligence. San Mateo, Calif: M. Kaufmann, 1988.

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P, O'Hare G. M., and Jennings Nick, eds. Foundations of distributed artificial intelligence. New York: Wiley, 1996.

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Book chapters on the topic "Distributed artificial intelligence"

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Mishra, Annu. "Distributed Artificial Intelligence." In Distributed Artificial Intelligence, 1–18. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-1.

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Singh, Pushpa, Rajnesh Singh, Narendra Singh, and Murari Kumar Singh. "A Distributed Artificial Intelligence." In Distributed Artificial Intelligence, 263–76. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-16.

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Yadav, Satya Prakash. "Distributed Consensus." In Distributed Artificial Intelligence, 69–84. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-5.

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Agarwal, Rashi, Supriya Khaitan, and Shashank Sahu. "Intelligent Agents." In Distributed Artificial Intelligence, 19–46. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-2.

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Seem, Ankur, Arpit Kumar Chauhan, Rijwan Khan, and Satya Prakash Yadav. "Distributed Artificial Intelligence for Document Retrieval." In Distributed Artificial Intelligence, 59–68. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-4.

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Gupta, Nidhi, Shailesh Singh, and Sonia Gupta. "Agora Architecture." In Distributed Artificial Intelligence, 157–78. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-10.

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Sankhyadhar, Shubhangi, and Mohit Pandey. "Test Beds for Distributed AI Research." In Distributed Artificial Intelligence, 179–94. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-11.

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Yadav, Vijay, Raghuraj Singh, and Vibhash Yadav. "Real-Time Framework Competitive Distributed Dilemma." In Distributed Artificial Intelligence, 195–216. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-12.

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Hussain, Naziya, and Preeti Rani. "Comparative Studied Based on Attack Resilient and Efficient Protocol with Intrusion Detection System Based on Deep Neural Network for Vehicular System Security." In Distributed Artificial Intelligence, 217–36. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-13.

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Rawat, Dhruv, Amit Kumar, and Suresh Kumar. "A Secure Electronic Voting System Using Decentralized Computing." In Distributed Artificial Intelligence, 237–50. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-14.

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Conference papers on the topic "Distributed artificial intelligence"

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Harmon, Laurel A., and Robert F. Franklin. "Distributed Artificial Intelligence: A Critical Review." In 1988 Robotics Conferences, edited by David P. Casasent, Ernest L. Hall, and Kenneth J. Stout. SPIE, 1989. http://dx.doi.org/10.1117/12.949134.

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Karthick, Av, and S. Gopalsamy. "Artificial Intelligence: Trends and Challenges." In 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2022. http://dx.doi.org/10.1109/pdgc56933.2022.10053238.

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Mohammed, Shady A., Shervin Shirmohammadi, and Sa'di Altamimi. "Artificial Intelligence-Based Distributed Network Latency Measurement." In 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2019. http://dx.doi.org/10.1109/i2mtc.2019.8826859.

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Kinney, Michael, and Costas Tsatsoulis. "Learning comunication strategies for distributed artificial intelligence." In Aerospace Sensing, edited by Firooz A. Sadjadi. SPIE, 1992. http://dx.doi.org/10.1117/12.139954.

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Aghaebrahimi, M. R., and M. Amiri. "Distributed generator placement techniques using artificial intelligence." In 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009. IEEE, 2009. http://dx.doi.org/10.1109/supergen.2009.5348248.

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Rumovskaya, Sophiya, and Andrey Litvin. "Capabilities of Distributed Artificial Intelligence in Medicine." In 2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2023. http://dx.doi.org/10.1109/summa60232.2023.10349394.

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Ruiz, Cristian C., Olivier A. Richard, Oleg Iegorov, and Brice Videau. "Managing Large Scale Experiments in Distributed Testbeds." In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.795-011.

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Sun, Yan, and Yunna Liu. "Application and Prospects of Artificial Intelligence in Intelligent Transportation Systems." In 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC). IEEE, 2023. http://dx.doi.org/10.1109/icirdc62824.2023.00088.

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Schmidt, Rainer, Alfred Zimmermann, Barbara Keller, and Michael Mohring. "Towards Engineering Artificial Intelligence-based Applications." In 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW). IEEE, 2020. http://dx.doi.org/10.1109/edocw49879.2020.00020.

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Dias, Hugo, Joao Rocha, Paulo Silva, Carlos Leao, and Luis Paulo Reis. "Distributed Surveillance System." In 2005 Purtuguese Conference on Artificial Intelligence. IEEE, 2005. http://dx.doi.org/10.1109/epia.2005.341225.

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Reports on the topic "Distributed artificial intelligence"

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Meyer, Robert A., and Susan E. Conry. Northeast Artificial Intelligence Consortium Annual Report 1986. Volume 3. Distributed Artificial Intelligence for Communication Network Management. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada197957.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
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