Статті в журналах: "Distributed artificial intelligence and multi-Agent systems"

1

Hock, Andreas, and Angela P. Schoellig. "Distributed iterative learning control for multi-agent systems." Autonomous Robots 43, no. 8 (March 27, 2019): 1989–2010. http://dx.doi.org/10.1007/s10514-019-09845-4.

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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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3

Lin, Hong, and Chunsheng Yang. "Specifying distributed multi-agent systems in chemical reaction metaphor." Applied Intelligence 24, no. 2 (April 2006): 155–68. http://dx.doi.org/10.1007/s10489-006-6936-x.

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4

Pooja, Dr. Manish Varshney. "The Study of Fundamental Concepts of Agent and Multi-agent Systems." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (November 4, 2023): 3237–38. http://dx.doi.org/10.52783/tjjpt.v44.i3.1592.

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The concept of n intelligent agent is a concept that is born from the area of artificial intelligence; in fact, a commonly-accepted definition relates the discipline of artificial intelligence with the analysis and design of autonomous entities capable of exhibitin intelligent behavior. From that perspective, it is assumed that an intelligent agent must be able to perceive its environment, reason about how to achieve its objectives, act towards achieving them through the application of some principle of rationality, and interact with other intelligent agents, being artificial or human [1]. Multi-agent systems are a particular case of a distributed system, and its particularity lies in the fact that the components of the system are autonomous and selfish, seeking to satisfy their own objectives. In addition, these systems also stand out for being open systems without a centralized design [2]. One main reason for the great interest and attention that multi-agent systems have received is that they are seen as an enabling technology for complex applications that require distributed and parallel processing of data and operate autonomously in complex and dynamic domains.

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5

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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6

RAMCHURN, SARVAPALI D., DONG HUYNH, and NICHOLAS R. JENNINGS. "Trust in multi-agent systems." Knowledge Engineering Review 19, no. 1 (March 2004): 1–25. http://dx.doi.org/10.1017/s0269888904000116.

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Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

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7

Braubach, Lars, Alexander Pokahr, Winfried Lamersdorf, Karl-Heinz Krempels, and Peer-Oliver Woelk. "A GENERIC TIME MANAGEMENT SERVICE FOR DISTRIBUTED MULTI-AGENT SYSTEMS." Applied Artificial Intelligence 20, no. 2-4 (February 2006): 229–49. http://dx.doi.org/10.1080/08839510500484231.

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8

Jaleel, Hanan Qassim, Jane Jaleel Stephan, and Sinan A. Naji. "Multi-Agent Systems: A Review Study." Ibn AL- Haitham Journal For Pure and Applied Sciences 33, no. 3 (July 20, 2020): 188. http://dx.doi.org/10.30526/33.3.2483.

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Multi-agent systems are subfield of Artificial Intelligence that has experienced rapid growth because of its flexibility and intelligence in order to solve distributed problems. Multi-agent systems (MAS) have got interest from various researchers in different disciplines for solving sophisticated problems by dividing them into smaller tasks. These tasks can be assigned to agents as autonomous entities with their private database, which act on their environment, perceive, process, retain and recall by using multiple inputs. MAS can be defined as a network of individual agents that share knowledge and communicate with each other in order to solve a problem that is beyond the scope of a single agent. It is imperative to understand the characteristics of the individual agent or computing entity to distinguish a simple distributed system and multi-agent system. To solve each task, the agent can take the appropriate actions, decisions, and can cooperate with other agents to fulfill its goals or objectives. MAS has several applications including cloud computing, web advertisement, e-commerce, computer network, learning abilities, etc. This survey represents a comprehensive discussion of all aspects of MAS, and discusses an overview of MAS definitions, architecture, features, communications, organizations, and finally discusses the some of the real practical applications.

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9

Dutta, P. S., N. R. Jennings, and L. Moreau. "Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems." Journal of Artificial Intelligence Research 24 (October 1, 2005): 407–63. http://dx.doi.org/10.1613/jair.1735.

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Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littman's Q-routing and Stone's TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.

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10

Zvyagin, L. S. "STUDYING MULTI-AGENT SYSTEMS AND SOLVING PROBLEMS OF THEIR MATHEMATICAL ALGORITHMIZATION." SOFT MEASUREMENTS AND COMPUTING 1, no. 9 (2021): 62–73. http://dx.doi.org/10.36871/2618-9976.2021.09.003.

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Today, multi-agent systems are one of the most important and promising areas of development, which are used in various fields. Multi-agent systems differ from traditional modeling methods in that the system works with each component separately. The agent-based approach helps to move from cumbersome calculations to parallel ones. The multi-agent approach can be used everywhere, in the construction of ERP systems that help in the management of distributed production processes, for the management of distributed production processes, with the help of multi-agent systems, it is also possible to solve problems related to artificial intelligence, since this system divides the task into several components and helps to solve the problem with the help of collective intelligence. Let's define the following concepts for clarity of presentation.

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11

POGGI, AGOSTINO, and PAOLA TURCI. "AN AGENT BASED LANGUAGE FOR THE DEVELOPMENT OF DISTRIBUTED SOFTWARE SYSTEMS." International Journal on Artificial Intelligence Tools 05, no. 03 (September 1996): 347–66. http://dx.doi.org/10.1142/s0218213096000237.

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This paper presents a concurrent object-oriented language, called CUBL, that seems be suitable for the development and maintenance of multi-agent systems. This language is based on objects, called c_units, that act in parallel and communicate with each other through synchronous and asynchronous message passing, and allows the distribution of a program, that is, of its objects on a network of UNIX workstations. This language has been enriched with an agent architecture that offers some of more important features for agent-oriented programming and some advantages as regards the other implemented agent architectures. In particular this architecture allows the development of systems where agents communicate with each other through a high level agent communication language and can change their behavior during their life.

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12

Wang, Aijuan, Tao Dong, and Xiaofeng Liao. "Distributed optimal consensus algorithms in multi-agent systems." Neurocomputing 339 (April 2019): 26–35. http://dx.doi.org/10.1016/j.neucom.2019.01.044.

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13

Horfan Álvarez, Daniel, Andrew Mark Bailey, and Lucas Adrián Gómez Blandón. "Distributed network multi agent security system. Net-Mass." Revista Facultad de Ingeniería Universidad de Antioquia, no. 34 (July 24, 2005): 101–13. http://dx.doi.org/10.17533/udea.redin.343174.

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Network security is one of the weakest and most sensitive areas within an organization, because it requires different strategies to protect vulnerable points as well as the coordination and distribution of efforts in order to encompass all the possible forms and points of computer attacks. In this article a distributed multiagent system is proposed as a tool to protect security in networks with different operating systems and which consequently are susceptible to diverse attacks. The system proposed is composed of heterogeneous autonomous agents with different characteristics in terms of their strategies of intruder detection and protection of primary network systems. It is planned that these agents will base their decisions on the application of different artificial intelligence techniques, such as expert system heuristics, evolutionary algorithms, and neural networks.

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MANI, NARIMAN, VAHID GAROUSI, and BEHROUZ H. FAR. "SEARCH-BASED TESTING OF MULTI-AGENT MANUFACTURING SYSTEMS FOR DEADLOCKS BASED ON MODELS." International Journal on Artificial Intelligence Tools 19, no. 04 (August 2010): 417–37. http://dx.doi.org/10.1142/s0218213010000261.

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Multi-Agent Systems (MAS) have been extensively used in the automation of manufacturing systems. However, similar to other distributed systems, autonomous agents' interaction in the Automated Manufacturing Systems (AMS) can potentially lead to runtime behavioral failures including deadlocks. Deadlocks can cause major financial consequences by negatively affecting the production cost and time. Although the deadlock monitoring techniques can prevent the harmful effects of deadlocks at runtime, but the testing techniques are able to detect design faults during the system design and development stages that can potentially lead to deadlock at runtime. In this paper, we propose a search based testing technique for deadlock detection in multi-agent manufacturing system based on the MAS design models. MAS design artifacts, constructed using Multi-agent Software Engineering (MaSE) methodology, are used for extracting test requirements for deadlock detection. As the case study, the proposed technique is applied to a multi-agent manufacturing system for verifying its effectiveness. A MAS simulator has been developed to simulate multi-agent manufacturing system behavior under test and the proposed testing technique has been implemented in a test requirement generator tool which creates test requirements based on the given design models.

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Ford, Kenneth M., James Allen, Niranjan Suri, Patrick J. Hayes, and Robert Morris. "PIM: A Novel Architecture for Coordinating Behavior of Distributed Systems." AI Magazine 31, no. 2 (July 28, 2010): 9. http://dx.doi.org/10.1609/aimag.v31i2.2261.

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Process integrated mechanisms (PIM) offer a new approach to the problem of coordinating the activity of physically distributed systems or devices. Current approaches to coordination all have well-recognized strengths and weaknesses. We propose a novel architecture to add to the mix, called the Process Integrated Mechanism (PIM), which enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to its rejection in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously. The PIM models offers promise as an effective infrastructure for handling tasks that require a high degree of time-sensitive coordination between the components, as well as a clean mechanism for coordinating the high-level goals of loosely coupled systems. PIM models enable coordination without the fragility and high communication overhead of centralized control, but also without the uncertainty associated with the system-level behavior of a MAS.The PIM model provides an ease of programming with advantages over both multi-agent sys-tems and centralized architectures. It has the robustness of a multi-agent system without the significant complexity and overhead required for inter-agent communication and negotiation. In contrast to centralized approaches, it does not require managing the large amounts of data that the coordinating process needs to compute a global view. In a PIM, the process moves to the data and may perform computations on the components where the data is locally available, sharing only the information needed for coordination of the other components. While there are many remaining research issues to be addressed, we believe that PIMs offer an important and novel tech-nique for the control of distributed systems.

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BRIAN BLAKE, M. "AGENT-BASED COMMUNICATION FOR DISTRIBUTED WORKFLOW MANAGEMENT USING JINI TECHNOLOGIES." International Journal on Artificial Intelligence Tools 12, no. 01 (March 2003): 81–99. http://dx.doi.org/10.1142/s0218213003001125.

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Agent communication has developed widely over the past decade for various types of multiple agent environments. Originally, most of this research surrounded simulation systems and inference systems. Subsequently, agents are expected to adapt to, dynamically create, and understand evolving conversation policies. This concept of agent communication is not completely necessary in some domains. One such domain is that of distributed workflow management with implications into Electronic Commerce. In this domain, agents are "middle-agents" that represent the distributed components that implement each individual workflow step. By representing the component-based services of each step, multiple distributed agents can essentially manage a workflow or supply chain that spans several online businesses (B2B). The WARP (Workflow-Automation through Agent-Based Reflective Processes) architecture is a multi-agent architecture developed to support distributed workflow management environments where distributed components are used to implement each of the workflow steps This paper describes an object-oriented workflow ontology for this distributed workflow management domain. There is also a software engineering process for integrating new component-based services into this ontology. Furthermore, the interaction protocol and supporting implementation based on the Knowledge Query and Manipulation Language (KQML) are presented. This agent communication architecture is implemented using Sun MicroSystems' Java and Jini technologies.

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Xi, Changjiang, and Jiuxiang Dong. "Event-triggered adaptive fuzzy distributed tracking control for uncertain nonlinear multi-agent systems." Fuzzy Sets and Systems 402 (January 2021): 35–50. http://dx.doi.org/10.1016/j.fss.2019.11.005.

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d'AVILA GARCEZ, ARTUR S., LUÍS C. LAMB, KRYSIA BRODA, and DOV M. GABBAY. "APPLYING CONNECTIONIST MODAL LOGICS TO DISTRIBUTED KNOWLEDGE REPRESENTATION PROBLEMS." International Journal on Artificial Intelligence Tools 13, no. 01 (March 2004): 115–39. http://dx.doi.org/10.1142/s0218213004001442.

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Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.

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Pereira, Helder, Bruno Ribeiro, Luis Gomes, and Zita Vale. "Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent Communities." Sustainability 14, no. 23 (November 30, 2022): 15983. http://dx.doi.org/10.3390/su142315983.

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The modeling of smart grids using multi-agent systems is a common approach due to the ability to model complex and distributed systems using an agent-based solution. However, the use of a multi-agent system framework can limit the integration of new operation and management models, especially artificial intelligence algorithms. Therefore, this paper presents a study of available open-source multi-agent systems frameworks developed in Python, as it is a growing programming language and is largely used for data analytics and artificial intelligence models. As a consequence of the presented study, the authors proposed a novel open-source multi-agent system framework built for smart grid modeling, entitled Python-based framework for heterogeneous agent communities (PEAK). This framework enables the use of simulation environments but also allows real integration at pilot sites using a real-time clock. To demonstrate the capabilities of the PEAK framework, a novel agent ecosystem based on agent communities is shown and tested. This novel ecosystem, entitled Agent-based ecosystem for Smart Grid modeling (A4SG), takes full advantage of the PEAK framework and enables agent mobility, agent branching, and dynamic agent communities. An energy community of 20 prosumers, of which six have energy storage systems, that can share energy among them, using a peer-to-peer market, is used to test and validate the PEAK and A4SG solutions.

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ANDRONACHE, VIRGIL, and MATTHIAS SCHEUTZ. "ADE — AN ARCHITECTURE DEVELOPMENT ENVIRONMENT FOR VIRTUAL AND ROBOTIC AGENTS." International Journal on Artificial Intelligence Tools 15, no. 02 (April 2006): 251–85. http://dx.doi.org/10.1142/s0218213006002655.

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In this paper we present the agent architecture development environment ADE, intended for the design, implementation, and testing of distributed agent architectures. After a short review of architecture development tools, we discuss ADE's unique features that place it in the intersection of multi-agent systems and development kits for single agent architectures. A detailed discussion of the general properties of ADE, its implementation philosophy, and its user interface is followed by examples from virtual and robotic domains that illustrate how ADE can be used for designing, implementing, testing, and running agent architectures.

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Wen, Jiayan, Chen Wang, and Guangming Xie. "Asynchronous distributed event-triggered circle formation of multi-agent systems." Neurocomputing 295 (June 2018): 118–26. http://dx.doi.org/10.1016/j.neucom.2018.03.018.

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22

Yan, Junxiu, Hui Yu, and Xiaohua Xia. "Distributed optimization of multi-agent systems with delayed sampled-data." Neurocomputing 296 (June 2018): 100–108. http://dx.doi.org/10.1016/j.neucom.2018.03.036.

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23

Fu, Qin, Lili Du, Guangzhao Xu, Jianrong Wu, and Pengfei Yu. "Consensus control for multi-agent systems with distributed parameter models." Neurocomputing 308 (September 2018): 58–64. http://dx.doi.org/10.1016/j.neucom.2018.04.051.

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Shi, Lei, Jinliang Shao, Mengtao Cao, and Hong Xia. "Distributed containment of heterogeneous multi-agent systems with switching topologies." Neurocomputing 312 (October 2018): 41–48. http://dx.doi.org/10.1016/j.neucom.2018.05.050.

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Zhao, Huanyu, Ju H. Park, Yulin Zhang, and Hao Shen. "Distributed output feedback consensus of discrete-time multi-agent systems." Neurocomputing 138 (August 2014): 86–91. http://dx.doi.org/10.1016/j.neucom.2014.02.043.

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PENDHARKAR, PARAG C., and RAHUL BHASKAR. "A HYBRID BAYESIAN NETWORK-BASED MULTI-AGENT SYSTEM AND A DISTRIBUTED SYSTEMS ARCHITECTURE FOR THE DRUG CRIME KNOWLEDGE MANAGEMENT." International Journal of Information Technology & Decision Making 02, no. 04 (December 2003): 557–76. http://dx.doi.org/10.1142/s0219622003000872.

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In this paper, we describe an approach for building a hybrid Bayesian network-based multi-agent system for drug crime knowledge management. We use distributed artificial intelligence architecture to create a multi-agent information system that integrates distributed knowledge sources and information to aid decision-making. Our comparison of the hybrid system with a previously developed stand-alone expert system Sherpa, which was in use at a large drug crime investigation facility, shows that the current system compares similar to the existing system in terms of efficiency and effectiveness of knowledge management. We illustrate how the proposed hybrid bayesian network-based can be implemented in the distributed computing network environment.

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Jennings, Nick R. "Commitments and conventions: The foundation of coordination in multi-agent systems." Knowledge Engineering Review 8, no. 3 (September 1993): 223–50. http://dx.doi.org/10.1017/s0269888900000205.

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AbstractDistributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the systems' overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism, and it is argued thatcommitments(pledges to undertake a specific course of action) andconventions(means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally, a number of prominent coordination techniques which do notexplicitlyinvolve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.

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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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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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Yu, Bo, Zi Xian Zhang, Yi Xiong Feng, Luis Ariel Diago, and Ichiro Hagiwara. "Cooperation Design System Based on Mobile-C Agent Platform." Advanced Engineering Forum 1 (September 2011): 16–20. http://dx.doi.org/10.4028/www.scientific.net/aef.1.16.

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Over the past decades, Distributed Systems (DS) have been adopted for industrial applications to improve the system efficiency because distributed architecture has advantages in resource utilization, fault toleration .etc. Multi-Agent System (MAS) arises from combination of the theories of artificial intelligence and distributed systems. One character of MAS is their self-organization, so how to implement an effective mechanism for self-organization of agents is important to a MAS system, this paper describes the design and implementation of a Mobile-C based agent management system, in which Mobile-C was adopted as the implementation platform, and this paper also described an agent-based cooperative design application using this system to manage all the agents involved.

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Feng, Tao, Huaguang Zhang, Yanhong Luo, and Hongjing Liang. "Globally optimal distributed cooperative control for general linear multi-agent systems." Neurocomputing 203 (August 2016): 12–21. http://dx.doi.org/10.1016/j.neucom.2016.04.011.

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32

Dao-Tran, Minh, Thomas Eiter, Michael Fink, and Thomas Krennwallner. "Distributed Evaluation of Nonmonotonic Multi-context Systems." Journal of Artificial Intelligence Research 52 (April 29, 2015): 543–600. http://dx.doi.org/10.1613/jair.4574.

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Multi-context Systems (MCSs) are a formalism for systems consisting of knowledge bases (possibly heterogeneous and non-monotonic) that are interlinked via bridge rules, where the global system semantics emerges from the local semantics of the knowledge bases (also called contexts) in an equilibrium. While MCSs and related formalisms are inherently targeted for distributed set- tings, no truly distributed algorithms for their evaluation were available. We address this short- coming and present a suite of such algorithms which includes a basic algorithm DMCS, an ad- vanced version DMCSOPT that exploits topology-based optimizations, and a streaming algorithm DMCS-STREAMING that computes equilibria in packages of bounded size. The algorithms be- have quite differently in several respects, as experienced in thorough experimental evaluation of a system prototype. From the experimental results, we derive a guideline for choosing the appropriate algorithm and running mode in particular situations, determined by the parameter settings.

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Park, Sankyu, Key-Sun Choi, and K. H. (Kane) Kim. "A Framework for Multi-Agent Systems with Multi-Modal User Interfaces in Distributed Computing Environments." International Journal of Software Engineering and Knowledge Engineering 07, no. 03 (September 1997): 351–69. http://dx.doi.org/10.1142/s0218194097000217.

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In current multi-agent systems, the user is typically interacting with a single agent at a time through relatively inflexible and modestly intelligent interfaces. As a consequence, these systems force the users to submit simplistic requests only and suffer from problems such as the low-level nature of the system services offered to users, the weak reusability of agents, and the weak extensibility of the systems. In this paper, a framework for multi-agent systems called the open agent architecture (OAA) which reduces such problems, is discussed. The OAA is designed to handle complex requests that involve multiple agents. In some cases of complex requests from users, the components of the requests do not directly correspond to the capabilities of various application agents, and therefore, the system is required to translate the user's model of the task into the system's model before apportioning subtasks to the agents. To maximize users' efficiency in generating this type of complex requests, the OAA offers an intelligent multi-modal user interface agent which supports a natural language interface with a mix of spoken language, handwriting, and gesture. The effectiveness of the OAA environment including the intelligent distributed multi-modal interface has been observed in our development of several practical multi-agent systems.

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34

Xie, Bing, Xueqiang Gu, Jing Chen, and LinCheng Shen. "A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems." International Journal of Advanced Robotic Systems 15, no. 6 (November 1, 2018): 172988141881303. http://dx.doi.org/10.1177/1729881418813037.

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In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

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35

Ren, Xiaoxing, Dewei Li, Yugeng Xi, and Haibin Shao. "Distributed Subgradient Algorithm for Multi-Agent Optimization With Dynamic Stepsize." IEEE/CAA Journal of Automatica Sinica 8, no. 8 (August 2021): 1451–64. http://dx.doi.org/10.1109/jas.2021.1003904.

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36

Si, Yuanchao, Deyuan Meng, and JinRong Wang. "Controllability of multi-agent systems with input and communication delays." Nonlinear Analysis: Modelling and Control 28 (March 1, 2023): 1–21. http://dx.doi.org/10.15388/namc.2023.28.31662.

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Distributed cooperative control of multi-agent systems is broadly applied in artificial intelligence in which time delay is of great concern because of its ubiquitous. This paper considers the controllability of leader-follower multi-agent systems with input and communication delays. For the first-order systems with input delay, neighbor-based protocol is adopted to realize the interactions among agents, yielding a system with delay existed in state and control input. New notions of interval controllability and interval structural controllability for the system are defined. Algebraic criterion is established for interval controllability, and graph-theoretic interpretation is put forward for the interval structural controllability. Results imply that input delay of the multi-agent systems has significant influence on the interval controllability and interval structural controllability. Corresponding conclusions are generalized to the first-order systems and the high-order ones with communication delays, respectively. Example is attached to illustrate the work.

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37

El Khalidi, Nihal, Faouzia Benabbou, and Nawal Sael. "Distributed parking management architecture based on multi-agent systems." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (December 1, 2021): 801. http://dx.doi.org/10.11591/ijai.v10.i4.pp801-809.

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<span>With the increase of the number of vehicles on the road, several traffic congestion problems arise in the big city, and this has a negative impact on the economy, environment and citizens. The time spent looking for a parking space and the traffic generated contributes to mobility and traffic management problems. Hence the need for smart parking management to help drivers to find vacant spaces in a car park in a shorter time. Today, researchers are considering scenarios in which a large amount of services can be offered and used by drivers and authorities to improve the management of the city's car parks and standards of quality of life. Based on literature on smart parking management system (SPMS), we have established the most important services needed such as reservation, orientation, synchronization, and security. The dynamic distributed and open aspect of the problem led us to adopt a multi-agent modeling to ensure continuous evolution and flexibility of the management system. In this conceptual paper, we propose to structure those services on a multi-agent system (MAS) that covers the whole functions of a distributed SPMS. Each service is provided as an autonomous agent, able to communicate and collaborate with the others to propose optimized parking space to customers.</span>

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38

Mrozek, Mirosław. "Multi-Agent Control System for the Movement of Uniaxial Objects." Solid State Phenomena 237 (August 2015): 183–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.237.183.

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Multi-agent systems are used mainly in IT solutions and control groups of robots. From the point of view of classical control architectures, they are a kind of distributed systems in which nodes perform advanced algorithms, usually associated with the technology of artificial intelligence, and they can be considered as agents. The article describes the multi-agents control system of objects of uniaxial movements. An example of such a system to control a repository with movable racks with electric motors is presented. Each rack acts as an agent through the implemented control of the resources of embedded microcontrollers. Such a system provides high quality control, guaranteeing long-lasting, trouble-free operation while maintaining the safety of both service and stored items.

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39

Estanyol, Francesc, Xavier Rafael, Roman Roset, Miguel Lurgi, Mariola Mier, and Magi Lluch-Ariet. "A Web-accessible distributed data warehouse for brain tumour diagnosis." Knowledge Engineering Review 26, no. 3 (July 28, 2011): 329–51. http://dx.doi.org/10.1017/s0269888911000142.

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AbstractCurrently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.

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40

Chen, Jiaxi, Junmin Li, and Xinxin Yuan. "Distributed fuzzy adaptive consensus for high-order multi-agent systems with an imprecise communication topology structure." Fuzzy Sets and Systems 402 (January 2021): 1–15. http://dx.doi.org/10.1016/j.fss.2020.03.018.

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41

Cruz Salazar, Luis Alberto, and Birgit Vogel-Heuser. "A CPPS-architecture and workflow for bringing agent-based technologies as a form of artificial intelligence into practice." at - Automatisierungstechnik 70, no. 6 (June 1, 2022): 580–98. http://dx.doi.org/10.1515/auto-2022-0008.

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Abstract Due to the increase in Artificial Intelligence in the production systems domain, Industry 4.0 (I4.0) experts must collaborate with autonomous systems. Industrial AI raises several concerns about existing standards, which provide guidelines and design patterns. One way to realize I4.0 systems are Industrial Agents (IAs) due to their inherent autonomy and collaboration. Multi-Agent Systems (MASs) are well suited for realizing distributed AI in I4.0 components. Considering the properties of IAs and existing standards, an MAS architecture is presented for flexible and intelligent Cyber-Physical Production Systems. The article compares I4.0 standardization efforts relevant to adapt AI in the form of IAs, illustrates how IA design patterns can be used, and introduces the Multi-Agent aRchitecture for Industrial Automation applying desigN patterNs practicEs “MARIANNE”. An implementation guideline is presented to put this CPPS into practice.

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42

Lin, Qiong, Yingjiang Zhou, Guo-Ping Jiang, Shengyu Ge, and Shuai Ye. "Prescribed-time containment control based on distributed observer for multi-agent systems." Neurocomputing 431 (March 2021): 69–77. http://dx.doi.org/10.1016/j.neucom.2020.12.030.

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43

Li, Peiming, Xiang Wu, Xiangyong Chsen, and Jianlong Qiu. "Distributed adaptive finite-time tracking for multi-agent systems and its application." Neurocomputing 481 (April 2022): 46–54. http://dx.doi.org/10.1016/j.neucom.2022.01.065.

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44

Liu, Jia, Liuxiao Guo, Manfeng Hu, and Yongqing Yang. "Distributed delay control of multi-agent systems with nonlinear dynamics: Stochastic disturbance." Neurocomputing 152 (March 2015): 164–69. http://dx.doi.org/10.1016/j.neucom.2014.11.007.

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45

Wu, Jie, Qun Deng, Tao Han, and Huai-Cheng Yan. "Distributed bipartite tracking consensus of nonlinear multi-agent systems with quantized communication." Neurocomputing 395 (June 2020): 78–85. http://dx.doi.org/10.1016/j.neucom.2020.02.017.

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46

Lin, Qiong, Yingjiang Zhou, Guo-Ping Jiang, Shengyu Ge, and Shuai Ye. "Prescribed-time containment control based on distributed observer for multi-agent systems." Neurocomputing 431 (March 2021): 69–77. http://dx.doi.org/10.1016/j.neucom.2020.12.030.

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47

Peng, Zhouhua, Dan Wang, Hao Wang, and Wei Wang. "Distributed cooperative tracking of uncertain nonlinear multi-agent systems with fast learning." Neurocomputing 129 (April 2014): 494–503. http://dx.doi.org/10.1016/j.neucom.2013.09.009.

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48

Goonatilleke, Sandali Thamalka, and Budditha Hettige. "Past, Present and Future Trends in Multi-Agent System Technology." Journal Européen des Systèmes Automatisés 55, no. 6 (December 31, 2022): 723–39. http://dx.doi.org/10.18280/jesa.550604.

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Multi-Agent System (MAS) technology is one of the cores and promising areas in the field of Artificial Intelligence (AI) as well as in the stream of Computer Science. The technology is comprised of multiple decision-making agents that exist in an environment to achieve common or conflicting goals. Multi-Agent System technology has a rapid growth and evolution due to its marvelous features such as flexibility and intelligence that are very useful when solving complex distributed problems. This paper focuses on the history and evolution of MAS technology, present applications, and future trends by addressing more detailed explanations about the foundations or key principles of Multi-Agent System technology such as agents, agent taxonomy, agent communication approaches, MAS development frameworks as well as the history of agent technology. The goal of this paper is to provide broad and comprehensive knowledge about Multi-Agents System technology.

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49

Ibrahim, Entidhar K., Abbas H. Issa, and Sabah A. Gitaffa. "Optimization and Performance Analysis of Fractional Order PID Controller for DC Motor Speed Control." Journal Européen des Systèmes Automatisés 55, no. 6 (December 31, 2022): 723–39. http://dx.doi.org/10.18280/jesa.550605.

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Анотація:

Multi-Agent System (MAS) technology is one of the cores and promising areas in the field of Artificial Intelligence (AI) as well as in the stream of Computer Science. The technology is comprised of multiple decision-making agents that exist in an environment to achieve common or conflicting goals. Multi-Agent System technology has a rapid growth and evolution due to its marvelous features such as flexibility and intelligence that are very useful when solving complex distributed problems. This paper focuses on the history and evolution of MAS technology, present applications, and future trends by addressing more detailed explanations about the foundations or key principles of Multi-Agent System technology such as agents, agent taxonomy, agent communication approaches, MAS development frameworks as well as the history of agent technology. The goal of this paper is to provide broad and comprehensive knowledge about Multi-Agents System technology.

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50

Cao, Hu, Yongqiang Bai, and Huagang Liu. "Distributed rigid formation control algorithm for multi‐agent systems." Kybernetes 41, no. 10 (October 12, 2012): 1650–61. http://dx.doi.org/10.1108/03684921211276819.

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