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Добірка наукової літератури з теми "Distributed artificial intelligence and multi-Agent systems"

Автор: Grafiati
Опубліковано: 12 жовтня 2024

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

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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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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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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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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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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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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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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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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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Анотація:
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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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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Дисертації з теми "Distributed artificial intelligence and multi-Agent systems"

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Hu, Yuebing, and 胡跃冰. "Consensus control of multi-agent systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4658657X.

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Malheiro, Maria Benedita Campos Neves. "Methodologies for Belief Revision in Multi-agent Systems." Phd thesis, Instituições portuguesas -- UP-Universidade do Porto -- -Faculdade de Engenharia -- -Departamento de Engenharia Electrotécnica e de Computadores, 1999. http://dited.bn.pt:80/29534.

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The goal of this thesis is twofold: first, we want to present the distributed belief accommodation and revision model for multi-agent systems that has been developed and, second, we wish to show its applicability to an appropriate domain. The Distributed Belief Accommodation & Revision model, called DeBAteR model, was developed for co-operative heterogeneous multi-agent systems used to model inherently dynamic distributed problems. In these systems, although the agents are able to detect changes both in the environment and in the problem specifications, each agent has only a partial view of the global picture. As a result the information that represents the current state of affairs is dynamic, incomplete and sometimes uncertain. This non-monotonic kind of data is called beliefs ? a belief is a piece of data that is held as correct as long as no contradicting evidence is found or presented. Each agent is expected to include an assumption based truth maintenance module for representing properly this type of data. Our main effort was concentrated on the task of maintaining the system's information, which consists of updating, revising and accommodating the represented beliefs. Belief updating is necessary for including the changes detected by the agents both in the environment and/or in the problem specifications. Belief revision is essential for solving the inconsistencies detected among the represented beliefs. Belief accommodation and revision is crucial for integrating the multiple disparate perspectives regarding the same data items, which may occur whenever there is overlap of expertise domains between the agents. In order to solve the information conflicts that result from the detection of inconsistencies between distinct beliefs or within multi-perspective beliefs we conceived the DeBATeR model. The DeBAteR is fully distributed, provides individual belief autonomy and is made of two methodologies: the pro-active belief accommodation and revision methodology and the delayed belief revision methodology. Whilst the first methodology is used to solve domain independent conflicts, the second methodology was devised for solving domain dependent conflicts. Both methodologies use argumentation for, in the case of the domain independent conflicts, choosing the most credible perspective between the existing multiple perspectives of a belief, and, in the case of the domain dependent conflicts, finding the best alternative belief support set for the affected concepts. These methodologies are distributed and their scope may be internal or collective. The DeBAteR model main contributions are: (i) the pro-active methodology conceived for solving domain independent conflicts and (ii) the capability, not only to represent and maintain individual beliefs and joint beliefs, but also to accommodate, rationally maintain and make use of multi-perspective beliefs. Finally, we describe the developed decision support multi-agent system for choosing adequate project locations, called DIPLOMAT ? Dynamic and Interactive Project Location Test bed, which has the ability of accommodating and revising the represented beliefs according to the DeBAteR model methodologies.
O objectivo desta dissertação é duplo: por um lado, pretendemos dar a conhecer o modelo de revisão e acomodação de crenças para sistemas multi-agente por nós desenvolvido e, por outro, procuramos ilustrar a sua validade descrevendo a aplicação que realizámos. O modelo, designado DeBAteR ? Distributed Belief Accommodation & Revision, destina-se a sistemas multi-agente cooperativos e heterogéneos que modelam problemas inerentemente distribuídos e dinâmicos. Neste tipo de sistemas, a informação que representa o ambiente é dinâmica (os agentes possuem a capacidade de constatar alterações no ambiente e/ou nas condições do problema) e, muitas vezes, incompleta (os agentes possuem visões parcelares da realidade) e/ou incerta. Este tipo de informação, de carácter não definitivo, designa-se por crenças ? uma crença é uma convicção tida como correcta enquanto não for posta em causa por alguma evidência. Cada agente, a fim de representar e manipular crenças, foi enriquecido com um módulo específico de manutenção de consistência baseado em suposições. O nosso esforço concentrou-se na tarefa de manutenção (actualização, revisão e acomodação) da informação do sistema. A actualização de crenças é essencial para incorporar as alterações que os agentes detectam no ambiente e/ou nas condições do problema. A revisão de crenças é indispensável para resolver inconsistências (conflitos) entre as crenças representadas. A acomodação e revisão simultânea de crenças é imprescindível para a integração das múltiplas perspectivas díspares que surgem em relação a um mesmo item de informação (crenças pluri-perspectiva) quando existe sobreposição de domínios de especialidade entre os agentes. Para tentar solucionar estes conflitos entre crenças concebemos um modelo distribuído que assegura autonomia individual de crença. O modelo de acomodação e revisão de crenças DeBAteR é composto por duas metodologias: a metodologia pró-activa de acomodação e revisão de crenças pluri-perspectiva e a metodologia retardada de revisão de crenças. A primeira, destina-se a resolver conflitos independentes do domínio e a segunda destina-se à resolução de conflitos dependentes do domínio. Estas metodologias são suportadas quase integralmente por um sistema de argumentação que procura, no caso dos conflitos independentes do domínio, escolher a perspectiva mais credível e, no caso dos conflitos dependentes do domínio, encontrar o melhor conjunto alternativo de suporte para os conceitos afectados. Esta actividade é descentralizada e pode decorrer quer no âmbito intra-agente, quer no âmbito inter-agente. É ainda de realçar: (i) o carácter pró-activo da resolução dos conflitos independentes do domínio (crenças pluri-perspectiva) e (ii) a capacidade de, não só, representar e manter crenças de âmbito individual (crenças suportadas por apenas um agente) e crenças conjuntas (crenças suportadas por vários agentes), mas também, de sintetizar, manter racionalmente e utilizar crenças pluri-perspectiva. Por último, descrevemos o sistema multi-agente de apoio à decisão no domínio da localização de empreendimentos desenvolvido, denominado DIPLOMAT ? Dynamic and Interactive Project Location Test bed, o qual possui a capacidade de acomodar e rever crenças de acordo com as metodologias concebidas no âmbito do modelo DeBAteR.
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Qi, Dehu. "Multi-agent systems : integrating reinforcement learning, bidding and genetic algorithms /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3060133.

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Gandon, Fabien. "Distributed artificial intelligence and knowledge management : ontologies and multi-agent systems for a corporate semantic web." Nice, 2002. http://www.theses.fr/2002NICE5773.

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This work concerns multi-agents systems for the management of a corporate semantic web based on an ontology. It was carried out in the context of the European project CoMMA focusing on two application scenarios: support technology monitoring activities and assist the integration of a new employee to the organisation. Three aspects were essentially developed in this work: the design of a multi-agents architecture supporting both scenarios, and the organisational top-down approach followed to identify the societies, the roles and the interactions of agents. The construction of the ontology O'CoMMA and the structuring of a corporate memory exploiting semantic Web technologies. The design and implementation of the sub-societies of agents dedicated to the management of the annotations and the ontology and of the protocols underlying these groups of agents, in particular techniques for distributing annotations and queries between the agents
Ce travail concerne les systèmes multi-agents pour la gestion d'un web sémantique d'entreprise basé sur une ontologie. Il a été effectué dans le cadre du projet Européen CoMMA se focalisant sur deux scénarios d'application: l'assistance aux activités de veille technologique et l'aide à l'insertion d'un nouvel employé dans une organisation. Trois aspects ont essentiellement été développés dans ce travail: la conception d'une architecture multi-agents assistant les deux scénarios, et l'approche organisationnelle descendante adoptée pour identifier les sociétés, les rôles et les interactions des agents. La construction de l'ontologie O'CoMMA et la structuration de la mémoire organisationnelle en exploitant les technologies du Web sémantique. La conception et l'implantation (a) des sous-sociétés d'agents chargées de la maintenance des annotations et de l'ontologie et (b) des protocoles supportant ces deux groupes d'agents, en particulier des techniques pour la distribution des annotations et des requêtes entre les agents
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FASANOTTI, Luca. "A Distributed Intelligent Maintenance System based on Artificial Immune Approach and Multi-Agent Systems." Doctoral thesis, Università degli studi di Bergamo, 2016. http://hdl.handle.net/10446/63690.

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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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Mihal, Bađonski. "Adaptable Java Agents (AJA) — a Tool for Programming o f Multi-Agent Systems." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2004. https://www.cris.uns.ac.rs/record.jsf?recordId=73368&source=NDLTD&language=en.

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The main goal of this thesis is the creation o f the tool agent-oriented programming tool AJA. AJA is the acronym for Adaptable Java Agents. AJA consists o f two programming languages:- A higher-level language used for the description of the main agent parts. This language is called HADL, which is the acronym for Higher Agent Definition Language.- A lower-level language used for the programming o f the agent parts defined in HADL. This language is called Java+. It is actually Java enriched with the constructs for accessing higher-level agent parts defined in HADL.A translator from AJA to Java is implemented in the practical part o f the thesis. AJA agents have the following features: - Agent communicates with other agents using a construct called negotiation. The messages sent can be encrypted or digitally signed in order to ensure the security of the system. - Agent possesses adaptable  parameters and neural nets that adapt themselves when the environment changes.- Agent has reflexes, which are the reactive component o f the agent architecture.- Agent can perform its actions parallel. Actions execution is synchronized.- Agent is accessible via Internet, because it acts as a simple HTTP server. People can use this way to communicate with an agent.- Agent has Java Swing based graphical user interface. Its owner uses this interface to communicate with the agent.- Because o f the fact that Java-i- language extends Java, it is possible to use all useful Java features in the implementation o f AJA agents (e.g. JDBC for the database access). The thesis also presents an original approach of integrating artificial intelligence techniques, such as neural nets, with a programming language. Having the artificial intelligence components as a part of the programming language runtime environment makes their use straightforward. A programmer uses the language constructs that are implemented using the artificial intelligencewithout the need for understanding their background and theory.The thesis contains eight chapters and three appendixes. In the first chapter, an overview of agents and multi-agent systems is given. The second chapter surveys existing agent-oriented programming languages and tools. The third chapter introduces AJA and describes the architecture of AJA agents. The syntax and semantics o f AJA languages HADL and Java+ is described in the fourth chapter. The fifth chapter presents adaptable AJA constructs in more details. To demonstrate and test the created tool, a case-study multi-agent system has been implemented in AJA. There are four personal digital assistant agents in the system. The sixth chapter describes the example agents and positively evaluates the tool. In the seventh chapter the related work and tools are analyzed and compared to AJA. The last chapter concludes the thesis. The first appendix describes the implementation details of the AJA to Java translator. The second appendix is a guide for the installation and usage of the implemented AJA to Java translator. Finally, the thirdappendix describes step by step how to translate, compile, run, and use the example agents. The thesis contains many references, which include almost all the most important and the most actual papers in the field. The reference list can be found at the end o f the thesis.
Glavni doprinos doktorske teze je napravljeni alat za programiranje agenata AJA . AJA - Adaptabilni Java Agenti je jezički alat za programsku implementaciju agenata Sastoji se od dva programska jezika:- Jezik višeg nivoa kojim se opisuju glavne kom ponente agenta. Ovaj jezik se naziva HADL - Higher Agent Definition Language.- Jezik nižeg nivoa koji služi za implementaciju pojedinih komponenti agenta specificiranih HADL jezikom . Ovaj jezik se najava Java+, jer je on zapravo programski jezik Java obogaćen konstrukcijama pomoću kojih je moguće pristupati komponentama agenta, definisanim u jezik u HADL.AJA agent poseduje sledeće osobine:- Sigurna kom unikacija sa drugim A JA agentim a koristeći mehanizam pregovaranja, šifrovanje i digitalno potpisivanje poruka.- Mogućnost adaptiranja na promene u okruženju u kom se nalazi, koristeći neuralne mreže i adaptabilne parametre.- Reaktivnost zasnovana n a kom ponenti zvanoj refleks.- Paralelno izvršavanje akcija agenta u z njihovu internu sinhronizaciju.- D ostupnost agenta preko Interneta. Agent se ponaša kao jednostavan HTTP server. Na ovaj način se drugim osobama omogućuje da komuniciraju sa agentom .- G rafički korisnički interfejs zasnovan n a Java Swing tehnologiji- Pošto se u program iranju agenta koristi Java+, moguće je uposliti sve pogodnosti Jave, kao što su na primer pristup bazama podataka koristeći JDBC , rad sa multimedijalnim sadržajem , itd.U tezi je predstavljen i originalni pristup integrisanja tehnika veštačke inteligencije sa program skim jezikom . U građujući kom ponente veštačke inteligencije u izvršnu okolinu je z ik a čini n jihovo korišćenje veom a jednostavnim . Programer ne mora da bude ekspert iz veštačke inteligencije a da pri tome koristi konstrukcije jezika koje su implementirane pomoću veštačke inteligencije. AJA specifikacija agenta se sastoji od HADL i Java+ delova. U tezi je implementiran prevodioc kojim se A JA specifikacija prevodi u skup klasa programskog jezika Java. Implementiran je i jedan multi-agentski sistem kojim se praktično pokazuje korišćenje i mogućnosti napravljenog alata D oktorska teza sadrži i detaljan pregled oblasti o agentskpj m etodologiji. O n a kruniše višegodišnji rad kandidata i njegovog mentora u ovoj sve značajnijoj oblasti računarstva.Teza sadrži o sam glava i tri dodatka. U prvoj glavi se opisuje oblast agenata i m ulti-agentskih sistem a. Pregled postojećih agentskih program skih jezik a i alata se daje u drugoj glavi. O pis A JA agenata i njihove arhitekture je dat u trećoj glavi teze. Četvrta glava se bavi sintaksom i sem antikom oba A JA jezika: H A D L -a i Jave+. Adaptabilni elem enti A JA agenata se opisuju u petoj glavi. U šestoj glavi je opisan m ulti-agentski sistem koji j e ujed n o i prim er prim ene A JA alata. A JA se sa drugim postojećim agentskim alatim a upoređuje u sedm oj glavi. Osma glava sadrži zaključak. N a kraju se u tri dodatka detaljno opisuju im plem entacija prevodioca A JA -e u Javu, instalacija prevodioca i korišćenje napravljenog m ulti-agentskog sistema respektivno. U doktorskom radu su korišćene i navedene brojne reference kojim a su obuhvaćeni gotovo svi najznačajniji i najaktuelniji radovi iz oblasti multi-agentskih sistema. Lista referenci je navedena na kraju teze.
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Xu, Duo. "An agent-based tool for China's express delivery SMEs." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40987784.

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9

Charrier, Rodolphe. "L'intelligence en essaim sous l'angle des systèmes complexes : étude d'un système multi-agent réactif à base d'itérations logistiques couplées." Phd thesis, Université Nancy II, 2009. http://tel.archives-ouvertes.fr/tel-00442841.

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L'intelligence en essaim constitue désormais un domaine à part entière de l'intelligence artificielle distribuée. Les problématiques qu'elle soulève touchent cependant à de nombreux autres domaines ou questions scientifiques. En particulier le concept d'essaim trouve pleinement sa place au sein de la science dites des ``systèmes complexes''. Cette thèse présente ainsi la conception, les caractéristiques et les applications d'un modèle original, le SMA logistique, pour le domaine de l'intelligence en essaim. Le SMA logistique trouve son origine en modélisation des systèmes complexes : il est en effet issu des réseaux d'itérations couplées dont nous avons adapté le modèle de calcul à l'architecture multi-agent. Ce modèle se fonde sur des principes communs à d'autres disciplines, comme la synchronisation et le contrôle paramétrique que nous plaçons au coeur des mécanismes d'auto-organisation et d'adaptation. Du point de vue mathématique, les applications logistiques sont à la base de la formalisation du comportement interne des agents constituant le SMA logistique, tout en prenant place dans un schéma ``influence-réaction''. L'environnement à base de champs est l'autre aspect fondamental du SMA logistique, en permettant la réalisation des interactions indirectes des agents et en jouant le rôle d'une structure de données pour le système. Les travaux décrits dans cette thèse donnent lieu à des applications principalement en simulation et en optimisation, comme c'est le cas pour la plupart des algorithmes du domaine de l'intelligence en essaim. L'intérêt et l'originalité du SMA logistique pour l'intelligence en essaim résident dans l'aspect générique de son schéma théorique qui permet de traiter avec un même modèle des phénomènes considérés a priori comme distincts dans la littérature : phénomènes de ``flocking'' et phénomènes stigmergiques ``fourmis'' à base de phéromones. Ce modèle répond ainsi à un besoin d'explication des mécanismes mis en jeu autant qu'au besoin d'en synthétiser les algorithmes générateurs.
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Massawe, Libe Valentine. "The design and development of multi-agent based RFID middleware system for data and devices management." Thesis, Bloemfontein : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/167.

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Thesis (D. Tech. (Electrical Engineering)) - Central University of technology, Free State, 2012
Radio frequency identification technology (RFID) has emerged as a key technology for automatic identification and promises to revolutionize business processes. While RFID technology adoption is improving rapidly, reliable and widespread deployment of this technology still faces many significant challenges. The key deployment challenges include how to use the simple, unreliable raw data generated by RFID deployments to make business decisions; and how to manage a large number of deployed RFID devices. In this thesis, a multi-agent based RFID middleware which addresses some of the RFID data and device management challenges was developed. The middleware developed abstracts the auto-identification applications from physical RFID device specific details and provides necessary services such as device management, data cleaning, event generation, query capabilities and event persistence. The use of software agent technology offers a more scalable and distributed system architecture for the proposed middleware. As part of a multi-agent system, application-independent domain ontology for RFID devices was developed. This ontology can be used or extended in any application interested with RFID domain ontology. In order to address the event processing tasks within the proposed middleware system, a temporal-based RFID data model which considers both applications’ temporal and spatial granules in the data model itself for efficient event processing was developed. The developed data model extends the conventional Entity-Relationship constructs by adding a time attribute to the model. By maintaining the history of events and state changes, the data model captures the fundamental RFID application logic within the data model. Hence, this new data model supports efficient generation of application level events, updating, querying and analysis of both recent and historical events. As part of the RFID middleware, an adaptive sliding-window based data cleaning scheme for reducing missed readings from RFID data streams (called WSTD) was also developed. The WSTD scheme models the unreliability of the RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. The WSTD scheme is capable of efficiently coping with both environmental variations and tag dynamics by automatically and continuously adapting its cleaning window size, based on observed readings.
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Книги з теми "Distributed artificial intelligence and multi-Agent systems"

1

Ferber, Jacques. Multi-agent systems: An introduction to distributed artificial intelligence. Harlow: Addison-Wesley, 1998.

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McEleney, Bryan. Multi-Agent Systems and Manufacturing Problems. Dublin: University College Dublin, 1998.

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3

S, Shamma Jeff, ed. Cooperative control of distributed multi-agent systems. Chichester, West Sussex, England: John Wiley & Sons, 2007.

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S, Shamma Jeff, ed. Cooperative control of distributed multi-agent systems. Chichester, West Sussex, England: John Wiley & Sons, 2007.

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5

1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (12th : 2000 : Baden-Baden, Germany), eds. Advances in artificial intelligence and engineering cybernetics: Multi-agent systems: theory and applications ... Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2000.

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Adelinde, Uhrmacher, and Weyns Danny, eds. Multi-agent systems: Simulation and applications. Boca Raton, Fla: Taylor & Francis, 2009.

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Yokoo, Makoto. Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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8

International Conference on Multi-Agent Systems (1st 1995 San Francisco, Calif.). ICMAS-95: Proceedings First International Conference on Multi-Agent Systems : June 12-14, 1995, San Francisco, California. Menlo Park: AAAI Press/The MIT Press, 1995.

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1958-, De Wilde Philippe, ed. Convergence and knowledge processing in multi-agent systems. New York: Springer, 2009.

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10

KES-AMSTA, 2011 (2011 Manchester England). Agent and multi-agent systems: Technologies and applications : 5th KES international conference, KES-AMSTA 2011, Manchester, UK, June 29-July 1, 2011 : proceedings. Berlin: Springer, 2011.

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Частини книг з теми "Distributed artificial intelligence and multi-Agent systems"

1

Lavendelis, Egons. "Multi-agent Architecture for Intelligent Insurance Systems." In Distributed Computing and Artificial Intelligence, 439–47. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00551-5_53.

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2

Santos, Gabriel, Tiago Pinto, Zita Vale, Hugo Morais, and Isabel Praça. "Upper Ontology for Multi-Agent Energy Systems’ Applications." In Distributed Computing and Artificial Intelligence, 617–24. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00551-5_73.

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3

Carlsson, Bengt. "An evolutionary model of multi-agent systems." In Distributed Artificial Intelligence Architecture and Modelling, 58–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61314-5_21.

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4

Soriano, Angel, Enrique J. Bernabeu, Angel Valera, and Marina Vallés. "Collision Avoidance of Mobile Robots Using Multi-Agent Systems." In Distributed Computing and Artificial Intelligence, 429–37. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00551-5_52.

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Zhou, Lingzhong, Abdul Sattar, and Scott Goodwin. "Handling Over-Constrained Problems in Distributed Multi-agent Systems." In Advances in Artificial Intelligence, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424918_2.

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6

Bernhard, Alexis T., Simon Jungbluth, Ali Karnoub, Aleksandr Sidorenko, William Motsch, Achim Wagner, and Martin Ruskowski. "I4.0 Holonic Multi-agent Testbed Enabling Shared Production." In Artificial Intelligence in Manufacturing, 231–50. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_13.

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AbstractThis chapter aims at presenting the system architecture of a distributed production testbed embedded in an interoperable Shared Production network. The goal of the modular architecture is to enable flexible, resilient and distributed production. The presented approach illustrates how Multi-Agent Systems (MAS) can be incorporated in the manufacturing domain for distributed components on different hierarchy layers based on a holonic approach. The concept is validated on the real-world demonstrator testbed of the SmartFactoryKL. Furthermore, the MAS is combined with Industry 4.0 technologies such as the Asset Administration Shell and OPC UA.
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Cavedon, Lawrence, and Gil Tidhar. "A logical framework for multi-agent systems and joint attitudes." In Distributed Artificial Intelligence Architecture and Modelling, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61314-5_18.

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Triantis, Tasos, and Panayiotis Pintelas. "An Integrated Environment for Building Distributed Multi-agent Educational Applications." In Artificial Intelligence: Methodology, Systems, and Applications, 351–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30106-6_36.

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Krausburg, Tabajara. "Hierarchical Coalition Formation in Multi-agent Systems." In Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, 210–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53829-3_23.

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Noda, Itsuki. "Framework of Distributed Simulation System for Multi-agent Environment." In PRICAI 2000 Topics in Artificial Intelligence, 811. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44533-1_100.

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Тези доповідей конференцій з теми "Distributed artificial intelligence and multi-Agent systems"

1

Facchini, Sante Dino. "Decentralized Autonomous Organizations and Multi-agent Systems for Artificial Intelligence Applications and Data Analysis." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/828.

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The Ph.D research project aims to explore the potential of the Decentralized Autonomous Organization paradigm in conjunction with classic software architectures for Artificial Intelligence applications. The intended goal is to investigate and formalize a possible integration path between Multi-agent System architectures and Decentralized Autonomous Organizations. Starting from the Foundation for Intelligent Physical Agents standards, we will extend basic primitives to integrate Multi-agent Systems on Distributed Ledger Technology networks. Possible deployment of services and applications in the Internet-of-Things, Artificial Intelligence and Distributed Machine Learning areas will be tested. Application of Data Analysis techniques on datasets built on such a framework will be also addressed.
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2

Leppanen, Teemu. "Distributed Artificial Intelligence with Multi-Agent Systems for MEC." In 2019 28th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2019. http://dx.doi.org/10.1109/icccn.2019.8846960.

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3

Choi, Young-Cheol, and Hyo-Sung Ahn. "The Bio-Insect and Artificial Robots Interaction Based on Multi-Agent Reinforcement Learning." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86688.

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Multi-agent reinforcement learning is a challenging research topic used in various fields including robotics, artificial intelligence, distributed control, and so on. Recently, there have been lots of efforts to develop multi-agent reinforcement learning theories; but there exist many difficulties in multi-agent reinforcement learning system. In this paper, we introduce our on-going project BRIDS (Bio-insect and artificial Robot Interaction based on Distributed System), which is for interactions between a bio-insect and artificial robots using multi-agent reinforcement learning. The main objective of this project is to drive the bio-insect to the desired position using a group of artificial intelligent robots. Simulation results show that artificial intelligent robots drive the bio-insect to the target position using reinforcement learning.
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4

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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5

"A MULTI-AGENT MPC ARCHITECTURE FOR DISTRIBUTED LARGE SCALE SYSTEMS." In 2nd International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002715805440551.

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6

Drawel, Nagat, Jamal Bentahar, Amine Laarej, and Gaith Rjoub. "Formalizing Group and Propagated Trust in Multi-Agent Systems." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/9.

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We present a formal framework that allows individual and group of agents to reason about their trust toward other agents. In particular, we propose a branching time temporal logic BT which includes operators that express concepts such as everyone trust, distributed trust and propagated trust. We analyze the satisfiability and model checking problems of this logic using a reduction technique.
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7

Xiong, Shixun, Qingxian Wu, and Yuhui Wang. "Distributed Coordination of Heterogeneous Multi-Agent Systems with Output Feedback Control." In 2019 International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI). IEEE, 2019. http://dx.doi.org/10.1109/icusai47366.2019.9124911.

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Liu, Xiaolu, Zhe Wang, Zongchuan Wang, Ping Song, and Duxin Chen. "Distributed Leaderless Impulsive Consensus of Nonlinear Multi-Agent Systems with Input Saturation." In 2019 China-Qatar International Workshop on Artificial Intelligence and Applications to Intelligent Manufacturing (AIAIM). IEEE, 2019. http://dx.doi.org/10.1109/aiaim.2019.8632774.

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Wei, Xiguang, Quan Li, Yang Liu, Han Yu, Tianjian Chen, and Qiang Yang. "Multi-Agent Visualization for Explaining Federated Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/960.

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As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.
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"COORDINATION OF PLANNING AND SCHEDULING TECHNIQUES FOR A DISTRIBUTED, MULTI-LEVEL, MULTI-AGENT SYSTEM." In 2nd International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002721001840191.

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Звіти організацій з теми "Distributed artificial intelligence and multi-Agent systems"

1

Doo, Johnny. The Use of eVTOL Aircraft for First Responder, Police, and Medical Transport Applications. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, September 2023. http://dx.doi.org/10.4271/epr2023020.

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<div class="section abstract"><div class="htmlview paragraph">Advancements in electric vertical takeoff and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry. One particularly promising application involves on-demand, rapid-response use cases to broaden first responders, police, and medical transport mission capabilities. With the dynamic and varying public service operations, eVTOL aircraft can offer potentially cost-effective aerial mobility components to the overall solution, including significant lifesaving benefits.</div><div class="htmlview paragraph"><b>Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects</b> discusses the challenges need to be addressed before identified capabilities and benefits can be realized at scale: <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Mission-specific eVTOL vehicle development </div></li><li class="list-item"><div class="htmlview paragraph">Operator- and patient-specific accommodations</div></li><li class="list-item"><div class="htmlview paragraph">Detect-and-avoid capabilities in complex and challenging operating environments</div></li><li class="list-item"><div class="htmlview paragraph">Autonomous and artificial intelligence-enhanced mission capabilities</div></li><li class="list-item"><div class="htmlview paragraph">Home-base charging systems for battery power platforms</div></li><li class="list-item"><div class="htmlview paragraph">Simplified operator and support training</div></li><li class="list-item"><div class="htmlview paragraph"> Vehicle/fleet maintenance and support</div></li><li class="list-item"><div class="htmlview paragraph">Acceptance and participation from stakeholder services, local and state-level leadership, field operators, and support team members</div></li></ul></div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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