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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

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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13

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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14

Korzeniewski, M., K. Kulikowski, J. Zakis, M. Jasinski, and A. Malinowski. "Implementation of a Web-based remote control system for qZS DAB application using low-cost ARM platform." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 4 (December 1, 2016): 887–96. http://dx.doi.org/10.1515/bpasts-2016-0097.

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Abstract Continuous development of intelligent network applications drives the demand for deployment-ready hardware and software solutions. Such solutions are highly valued not only by distributed producers of energy but by energy consumers as well. The use of intelligent network applications enables the development and improvement of the quality of services. It also increases self-sufficiency and efficiency. This paper describes an example of such device that allows for the control of a dual active bridge (DAB) converter and enables its remote control in real time over an IP-based network. The details of both hardware and software components of proposed implementation are provided. The DAB converter gives a possibility to control and manage the energy between two DC power systems with very different voltage levels. Not only information, but also the quality of energy, the direction of power flow, and energy storage systems can be easily controlled through an IP-based network and power electronics converters. Information technology, together with intelligent control of power electronics technology, provides a flexible solution, especially for sustainable smart grids.

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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

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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43

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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44

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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45

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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46

Grinshpoun, T., A. Grubshtein, R. Zivan, A. Netzer, and A. Meisels. "Asymmetric Distributed Constraint Optimization Problems." Journal of Artificial Intelligence Research 47 (July 30, 2013): 613–47. http://dx.doi.org/10.1613/jair.3945.

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Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model. The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new framework bridges the gap between multi-agent problems which tend to have asymmetric structure and the standard symmetric DCOP model. The benefits of the proposed model over previous attempts to generalize the DCOP model are discussed and evaluated. Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail. These include complete algorithms that have a substantial advantage in terms of runtime and network load over existing algorithms (for standard DCOPs) which use alternative representations. Moreover, standard incomplete algorithms (i.e., local search algorithms) are inapplicable to the existing DCOP representations of asymmetric constraints and when they are applied to the new ADCOP framework they often fail to converge to a local optimum and yield poor results. The local search algorithms proposed in the present paper converge to high quality solutions. The experimental evidence that is presented reveals that the proposed local search algorithms for ADCOPs achieve high quality solutions while preserving a high level of privacy.

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47

HAMADI, YOUSSEF. "CONFLICTING AGENTS IN DISTRIBUTED SEARCH." International Journal on Artificial Intelligence Tools 14, no. 03 (June 2005): 459–75. http://dx.doi.org/10.1142/s021821300500220x.

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We extend here the work on interleaved distributed graph based backjumping, IDIBT/GBJ7,6,5 by considering conflicting agents.13 The resulting method IDIBT/CBJ-DkC for Conflict-directed Back-Jumbing and Directed k-Consistency combines distributed and parallel explorations of search spaces with more efficient backtrack steps. We take advantage of the asynchronous framework to implement a more refined and efficient update of conflicts. A fine analysis of these conflicts allows to some extent the detection of k-inconsistent values. Our experiments show a large improvement over graph based distributed/interleaved search.

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48

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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49

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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50

Khoualdi, Kamel, and Michel Dumas. "An Alarm Processing System Using Distributed Artificial Intelligence Techniques." IFAC Proceedings Volumes 27, no. 5 (June 1994): 755–60. http://dx.doi.org/10.1016/s1474-6670(17)48116-x.

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