Relevant bibliographies by topics / Distributed artificial intelligence (DAI)

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 (DAI)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Distributed artificial intelligence (DAI)"

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Kåhre, Peter. "Luhmanns massmedieteori och Internet som ett artificiellt intelligent semiotiskt system [Luhmann’s mass-media theory and Internet as an artificial intelligent semiotic system]." MedieKultur: Journal of media and communication research 26, no. 49 (November 26, 2010): 13. http://dx.doi.org/10.7146/mediekultur.v26i49.2576.

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The article discusses how a modern form of AI programming, known as Connectionism in a design known as Distributed Artificial Intelligence (DAI), affects the perception Luhmann has on mass media's role concerning second-order observations. DAI uses nodes to create activity in the systems and not the codes used by the Classic or Symbolic form of AI. Luhmann’s theory can be developed by replacing the systems codes with nodes that change depending on their relations to other nodes. In this way, we can reformulate the concept of communication, so that it includes the systems interactions with the environment. It creates better conditions so that observing opportunities may arise directly from these interactions. Internet and AI-programmed search systems and robots can then act as an artificial semiotic system that creates opportunities for making observations.
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Xie, Guangqiang, Junyu Chen, and Yang Li. "Hybrid-order Network Consensus for Distributed Multi-agent Systems." Journal of Artificial Intelligence Research 70 (January 25, 2021): 389–407. http://dx.doi.org/10.1613/jair.1.12061.

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As an important field of Distributed artificial intelligence (DAI), multi-agent systems (MASs) have attracted the attention of extensive research scholars. Consensus as the most important issue in MAS, much progress has been made in studying the consensus control of MAS, but there are some problems remained largely unaddressed which cause the MAS to lose some useful network structure information. First, multi-agent consensus protocol usually proceeds over the low-order structure by only considering the direct edges between agents, but ignores the higher-order structure of the whole topology network. Second, the existing work assumes all the edges in a topology network have the same weight without exploring the potential diversity of the connections. In this way, multi-agent systems fail to enforce consensus, resulting in fragmentation into multiple clusters. To address the above issues, this paper proposes a Motif-aware Weighted Multi-agent System (MWMS) method for consensus control. We focus more on triangle motif in the network, but it can be extended to other kinds of motifs as well. First, a novel weighted network is used which is the combination of the edge-based lower-order structure and the motif-based higher-order structure, i.e., hybrid-order structure. Subsequently, by simultaneously considering the quantity and the quality of the connections in the network, a novel consensus framework for MAS is designed to update agents. Then, two baseline consensus algorithms are used in MWMS. In our experiments, we use ten topologies of different shapes, densities and ranges to comprehensively analyze the performance of our proposed algorithms. The simulation results show that the hybrid higher-order network can effectively enhance the consensus of the multi-agent system in different network topologies.
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Bian, Cheng-Gang, Wen Cao, and Gunnar Hartvigsen. "ViSe2 - An Agent-Based Expert Consulting System with Efficient Cooperation." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 3 (June 20, 1998): 104–10. http://dx.doi.org/10.20965/jaciii.1998.p0104.

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ViSe2 l is an expert consulting system which employs software agents to manage distributed knowledge sources. These individual software agents solve users’ problems either by themselves or via cooperation. The efficiency of cooperation plays a serious role in Distributed Problem Solving (DPS) and Multi-Agent Systems (MAS). We have focused on the development of a twin-base approach for agents to model the capabilities of each other, and thus achieve efficient cooperation. The current version of the ViSe2 implementation is an experimental model of an agent-based expert system. Compared with other cooperation approaches in Distributed Artificial Intelligence (DAI) area, the results received so far indicate that the ViSe2 agents serve their users in an efficient cooperation manner.
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PAPAZOGLOU, MIKE P., STEVEN C. LAUFMANN, and TIMOS K. SELLIS. "AN ORGANIZATIONAL FRAMEWORK FOR COOPERATING INTELLIGENT INFORMATION SYSTEMS." International Journal of Cooperative Information Systems 01, no. 01 (March 1992): 169–202. http://dx.doi.org/10.1142/s0218215792000088.

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There is a growing belief that the next generation of information processing systems will be based on the paradigm of Intelligent and Cooperative Information Systems (ICISs). Such systems will involve information agents — distributed over the nodes of a common communication network — which work in a synergistic manner by exchanging information and expertise, coordinating their activities and negotiating how to solve parts of a common information-intensive problem. Along with motivating the importance of such systems the paper gives an overview of related research areas, notably Distributed Artificial Intelligence (DAI) and Distributed Databases (DDBs), and presents a generic architecture which views an ICIS as a community of communicating and cooperating intelligent information agents.
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Gao, Fei, Xiong Hu Zhao, Hong Shan Liu, Zhi Kun Liu, and Jun Zheng Yang. "The Study of Drilling Safe Monitoring System Based on Multi Agent Group." Advanced Materials Research 512-515 (May 2012): 2565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.2565.

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Lots of complexity and uncertainties are existed in drilling process. Further more, one of the possible ways to solve on-site drilling safety accidents is the early-warning through real-time monitoring. Based on the analysis of main hazards during drilling process, considering that most of the hazards are observable and controllable, by using multi agent group (MAG) technology of distributed artificial intelligence (DAI), this paper puts forward a functional schematic model with drilling hazards monitoring multi agent group, which includes real-time monitoring agent, feature extraction agent warning analysis agent, communication agent and user interaction agent. Considering the features of drilling engineering hazard monitoring, a distributed drilling safety monitoring system including the two levels, site-level and base-level, is built.
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Liu, Ye, and Shou Xiang Wang. "A Development Framework of Smart Grid Control System Based on MAS." Advanced Materials Research 614-615 (December 2012): 807–10. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.807.

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In this paper, MAS is used to model the framework of smart grid. Multi-Agent Systems (MAS) is one of the popular method in Distributed Artificial Intelligence (DAI) in the past years, which is a good tools to simulate and model the complex systems. Several MAS develop platforms are exist. Considering that JADE (Java Agent Development Framework) is one of the helpful and convenient developing environments of MAS, JADE is adapted to develop smart grid control framework. In this paper, a suitable frame construction of the Multi-Agent Systems for the current power system is designed, which is applied to the smart grid with excellent adaptability and flexibility.
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Aityacine, Fatiha, Badr Hssina, and Belaid Bouikhalene. "JADE Multi-agent Middleware Applied to Contribute to Certificate Management of Students." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 1 (October 1, 2015): 176. http://dx.doi.org/10.11591/tijee.v16i1.1601.

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In this article, we present a multi-agent approach that aims to design, modeling and implementation of an application "smart school". Indeed Several institutions adopt the computerized management of education to meet the needs of students using multi-agent systems. They have the ability to act simultaneously in a shared environment. The purpose of this approach is to automate some administrative services of education, based on the theory of distributed artificial intelligence (DAI) and multi-agent systems (MAS). This multi-agent application integrates entities called agents that cooperate and communicate them to perform specific tasks. Our system is based on the middleware JADE (Java Agent DEvelopment Framework) used for the implementation and agents management. This model based on multi-agent systems is tested on the personal data of an experiment conducted with the students of Sultan Moulay Slimane University in Beni Mellal.
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Cliff, Oliver, Mikhail Prokopenko, and Robert Fitch. "Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems." Entropy 20, no. 2 (January 23, 2018): 51. http://dx.doi.org/10.3390/e20020051.

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The Kullback–Leibler (KL) divergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are applicable when selecting a candidate model for a distributed system, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables; however, we can exploit the properties of certain dynamical systems to formulate exact methods based on differential topology. We approach the problem by using reconstruction theorems to derive an analytical expression for the KL divergence of a candidate DAG from the observed dataset. Using this result, we present a scoring function based on transfer entropy to be used as a subroutine in a structure learning algorithm. We then demonstrate its use in recovering the structure of coupled Lorenz and Rössler systems.
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Berenjian, Golnaz, Homayun Motameni, Mehdi Golsorkhtabaramiri, and Ali Ebrahimnejad. "Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 251–72. http://dx.doi.org/10.3233/jifs-201696.

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Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.
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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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Dissertations / Theses on the topic "Distributed artificial intelligence (DAI)"

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Adolfsson, Victor. "The State of the Art in Distributed Mobile Robotics." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6195.

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Distributed Mobile Robotics (DMR) is a multidisciplinary research area with many open research questions. This is a survey of the state of the art in Distributed Mobile Robotics research. DMR is sometimes referred to as cooperative robotics or multi-robotic systems. DMR is about how multiple robots can cooperate to achieve goals and complete tasks better than single robot systems. It covers architectures, communication, learning, exploration and many other areas presented in this master thesis.
Uppsatsen är en brett spektrum på vilken forskning som pågår rörande distribuerad mobil robotik, dvs hur många robotar kan samverka för att lösa uppgifter.
Adress: Victor Adolfsson Folkparksvägen 12:24 372 38 Ronneby Telefon: 0457-171 42 Mobil: 0709-441389
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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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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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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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Goel, Anuj. "Symbolic model checking techniques for BDD-based planning in distributed environments." Full text (PDF) from UMI/Dissertation Abstracts International, 2002. http://wwwlib.umi.com/cr/utexas/fullcit?p3077646.

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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 (DAI)"

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Grace, David, and Honggang Zhang. Cognitive communications: Distributed artificial intelligence (DAI), regulatory policy and economics, implementation. Hoboken, N.J: Wiley, 2012.

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Australian, Workshop on DAI (1st 1995 Canberra A. C. T. ). Distributed artificial intelligence: Architecture and modelling : First Australian Workshop on DAI, Canberra, ACT, Australia, November 13, 1995 : proceedings. Berlin: Springer, 1996.

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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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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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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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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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Zhang, Chengqi, and Dickson Lukose, eds. Distributed Artificial Intelligence Architecture and Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61314-5.

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Tuba, Kocatürk, and Medjdoub Benachir, eds. Distributed intelligence in design. Hoboken, NJ: Blackwell, 2011.

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Kanerva, Pentti. Sparse distributed memory. Cambridge, Mass: MIT Press, 1988.

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Book chapters on the topic "Distributed artificial intelligence (DAI)"

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Kumar, Anuj, Satya Prakash Yadav, and Sugandha Mittal. "DAI for Document Retrieval." In Distributed Artificial Intelligence, 251–62. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-15.

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Mishra, Annu, and Satya Prakash Yadav. "DAI for Information Retrieval." In Distributed Artificial Intelligence, 85–100. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-6.

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Costantini, Stefania, Giovanni De Gasperis, and Giulio Nazzicone. "Exploration of Unknown Territory via DALI Agents and ASP Modules." In Distributed Computing and Artificial Intelligence, 12th International Conference, 285–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19638-1_33.

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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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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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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 (DAI)"

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Yang, Qin. "Self-Adaptive Swarm System (SASS)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/722.

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Abstract:
Distributed artificial intelligence (DAI) studies artificial intelligence entities working together to reason, plan, solve problems, organize behaviors and strategies, make collective decisions and learn. This Ph.D. research proposes a principled Multi-Agent Systems (MAS) cooperation framework -- Self-Adaptive Swarm System (SASS) -- to bridge the fourth level automation gap between perception, communication, planning, execution, decision-making, and learning.
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Pianpak, Poom, Tran Cao Son, Z. O. Toups, and William Yeoh. "A distributed solver for multi-agent path finding problems." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357702.

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Wu, Feng, Shlomo Zilberstein, and Nicholas R. Jennings. "Stochastic multi-agent planning with partial state models." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357699.

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Suryanarayana, Sharadhi Alape, David Sarne, and Sarit Kraus. "Information disclosure and partner management in affiliate marketing." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357703.

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Zimmer, Matthieu, and Paul Weng. "An efficient reinforcement learning algorithm for learning deterministic policies in continuous domains." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357704.

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Gershtein, Michael, David Sarne, and Yonatan Aumann. "Approval voting with costly information." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357705.

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Hong, Weijun, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, and Peng Sun. "Generative adversarial exploration for reinforcement learning." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357706.

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Zhou, Ming, Yong Chen, Ying Wen, Yaodong Yang, Yufeng Su, Weinan Zhang, Dell Zhang, and Jun Wang. "Factorized Q-learning for large-scale multi-agent systems." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357707.

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Xu, Junping, Xin He, and Dengji Zhao. "Double auction design on networks." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357708.

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Liu, Yu-Ren, Yi-Qi Hu, Hong Qian, and Yang Yu. "Asynchronous classification-based optimization." In DAI '19: First International Conference on Distributed Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356464.3357709.

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Reports on the topic "Distributed artificial intelligence (DAI)"

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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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Journal articles Dissertations / Theses Books
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