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Journal articles on the topic 'Multi-agent interaction'

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Author: Grafiati

Published: 25 May 2024

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1

Lorkiewicz,, Wojciech, and Radosław Katarzyniak. "Multi-participant Interaction in Multi-agent Naming Game." Computational Methods in Science and Technology 20, no. 2 (2014): 59–60. http://dx.doi.org/10.12921/cmst.2014.20.02.59-80.

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2

Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

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3

Penner, Robin R. "Multi-Agent Societies for Collaborative Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 15 (October 1996): 762–66. http://dx.doi.org/10.1177/154193129604001503.

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The application of a multi-agent architecture to the design and operation of automated process management systems is proving to be a fruitful method of facilitating human-system collaboration. The agent architecture we are developing is intended to be applied in environments where humans and automated systems jointly perform information intensive tasks, and is based on an organization of multiple agents, where both human and software agents are integrated members in groups akin to human societies. Important features of our architecture include an organization based on social structures, a user interface model based on a collaborative interaction metaphors, and a situated action paradigm for agent behavior.

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4

Boella, Guido, Joris Hulstijn, and Leendert van der Torre. "Interaction in Normative Multi-Agent Systems." Electronic Notes in Theoretical Computer Science 141, no. 5 (December 2005): 135–62. http://dx.doi.org/10.1016/j.entcs.2005.05.020.

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5

CHEREMISINOV, Dmitri. "The Specification of Agent Interaction in Multi-Agent Systems." Intelligent Information Management 01, no. 02 (2009): 65–72. http://dx.doi.org/10.4236/iim.2009.12011.

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6

Dushkin, Roman. "Multi-agent systems for cooperative ITS." Тренды и управление, no. 1 (January 2021): 42–50. http://dx.doi.org/10.7256/2454-0730.2021.1.34169.

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This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.

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Murakami, Yohei, Toru Ishida, Tomoyuki Kawasoe, and Reiko Hishiyama. "Multi-Agent Simulation Based on Interaction Design." Transactions of the Japanese Society for Artificial Intelligence 18 (2003): 278–85. http://dx.doi.org/10.1527/tjsai.18.278.

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8

Poslad, Stefan. "Specifying protocols for multi-agent systems interaction." ACM Transactions on Autonomous and Adaptive Systems 2, no. 4 (November 2007): 15. http://dx.doi.org/10.1145/1293731.1293735.

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Zhou, Wenhong, Jie Li, Yiting Chen, and Lin-Cheng Shen. "Strategic Interaction Multi-Agent Deep Reinforcement Learning." IEEE Access 8 (2020): 119000–119009. http://dx.doi.org/10.1109/access.2020.3005734.

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10

Vogel-Heuser, Birgit, Matthias Seitz, Luis Alberto Cruz Salazar, Felix Gehlhoff, Alaettin Dogan, and Alexander Fay. "Multi-agent systems to enable Industry 4.0." at - Automatisierungstechnik 68, no. 6 (June 25, 2020): 445–58. http://dx.doi.org/10.1515/auto-2020-0004.

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AbstractThe discussion paper “I4.0 language: vocabulary, message structure and semantic interaction protocols of the I4.0 language”, published by the working group “Semantics and Interaction of Industry 4.0 Components” of the GMA, also known as UAG of the AG 1 of the platform Industry 4.0 (I4.0), presented a concept for the language between I4.0 components. The main conclusion is: The increasing networking and cooperation of components enable new forms of organization and control. A clear understanding of machine interactions paves self-organized and self-optimized value creation in I4.0. Agent-based systems are an option for the realization of such I4.0 architectures. Due to their features, software agents are particularly well suited for representing I4.0 components and enabling I4.0 interactions. Agents are not only able to understand the necessary machine languages, but also the essential mechanisms for self-organization and self-optimization in value creation. The paper focuses on I4.0 scenarios described by the Platform I4.0 that describes challenges for the industry towards its digital future and demonstrates how emerging challenges in the area of I4.0 can be met with the help of agent-based systems.

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11

Sherstyugina, Anastasiya, and Roman Nesterov. "Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations." Proceedings of the Institute for System Programming of the RAS 35, no. 3 (2023): 11–32. http://dx.doi.org/10.15514/ispras-2023-35(3)-1.

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The structure of a process model directly discovered from an event log of a multi-agent system often does not reflect the behavior of individual agents and their interactions. We suggest analyzing the relations between events in an event log to localize actions executed by different agents and involved in their asynchronous interaction. Then, a process model of a multi-agent system is composed from individual agent models between which we add channels to model the asynchronous message exchange. We consider agent interaction within the acyclic and cyclic behavior of different agents. We develop an algorithm that supports the analysis of event relations between different interacting agents and study its correctness. Experimental results demonstrate the overall improvement in the quality of process models discovered by the proposed approach in comparison to monolithic models discovered directly from event logs of multiagent systems.

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12

Olaru, Andrei, and Monica Pricope. "Multi-Modal Decentralized Interaction in Multi-Entity Systems." Sensors 23, no. 6 (March 15, 2023): 3139. http://dx.doi.org/10.3390/s23063139.

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Current multi-agent frameworks usually use centralized, fixed communication infrastructures for the entities that are deployed using them. This decreases the robustness of the system but is less challenging when having to deal with mobile agents that can migrate between nodes. We introduce, in the context of the FLASH-MAS (Fast and Lightweight Agent Shell) multi-entity deployment framework, methods to build decentralized interaction infrastructures which support migrating entities. We discuss the WS-Regions (WebSocket Regions) communication protocol, a proposal for interaction in deployments using multiple communication methods, and a mechanism to facilitate using arbitrary names for entities. The WS-Regions Protocol is compared against Jade (the Java Agent Development Framework), the most popular agent deployment framework, with a favorable trade-off between decentralization and performance.

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13

Sarraf Shirazi, Abbas, Sebastian von Mammen, and Christian Jacob. "Abstraction of agent interaction processes: Towards large-scale multi-agent models." SIMULATION 89, no. 4 (March 5, 2013): 524–38. http://dx.doi.org/10.1177/0037549712470733.

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14

Ko, Kwang-Eun, Jeong-Soo Lee, In-Hun Jang, and Kwee-Bo Sim. "Design of network for data interaction between Robot Agents in Multi Agent Robot System (MARS)." Journal of Korean Institute of Intelligent Systems 17, no. 5 (October 25, 2007): 712–17. http://dx.doi.org/10.5391/jkiis.2007.17.5.712.

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15

Liu, Yong, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, and Yang Gao. "Multi-Agent Game Abstraction via Graph Attention Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7211–18. http://dx.doi.org/10.1609/aaai.v34i05.6211.

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In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.

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16

HU, Jiang-Ping, Zhi-Xin LIU, Jin-Huan WANG, Lin WANG, and Xiao-Ming HU. "Estimation, Intervention and Interaction of Multi-agent Systems." Acta Automatica Sinica 39, no. 11 (2013): 1796. http://dx.doi.org/10.3724/sp.j.1004.2013.01796.

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17

Wang, Lin, Xiaofan Wang, and Xiaoming Hu. "Synchronization of multi-agent systems with topological interaction." IFAC Proceedings Volumes 44, no. 1 (January 2011): 14642–47. http://dx.doi.org/10.3182/20110828-6-it-1002.02244.

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18

KATAGAMI, Daisuke, Hidefumi OHMURA, Yoshiaki YASUMURA, and Katsumi NITTA. "10209 Multi User Learning Agent for Social Interaction." Proceedings of Conference of Kanto Branch 2005.11 (2005): 317–18. http://dx.doi.org/10.1299/jsmekanto.2005.11.317.

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HU, Jiang-Ping, Zhi-Xin LIU, Jin-Huan WANG, Lin WANG, and Xiao-Ming HU. "Estimation, Intervention and Interaction of Multi-agent Systems." Acta Automatica Sinica 39, no. 11 (November 2013): 1796–804. http://dx.doi.org/10.1016/s1874-1029(13)60078-6.

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20

Cantwell, John. "A Formal Model of Multi-Agent Belief-Interaction." Journal of Logic, Language and Information 14, no. 4 (October 2005): 397–422. http://dx.doi.org/10.1007/s10849-005-4019-8.

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21

Cantwell, John. "A Formal Model of Multi-Agent Belief-Interaction." Journal of Logic, Language and Information 15, no. 4 (September 6, 2006): 303–29. http://dx.doi.org/10.1007/s10849-006-3776-3.

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22

Mariani, Stefano, and Andrea Omicini. "Special Issue “Multi-Agent Systems”: Editorial." Applied Sciences 10, no. 15 (August 1, 2020): 5329. http://dx.doi.org/10.3390/app10155329.

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Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on the environment. The relevance of MAS is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across many different application scenarios and business domains, ranging from logistics to social sciences, from robotics to supply chain, and more. The reason behind such a widespread and diverse adoption lies in MAS great expressive power in modeling and actually supporting operational execution of a variety of systems demanding decentralized computations, reasoning skills, and adaptiveness to change, which are a perfect fit for MAS central notions introduced above. This special issue gathers 11 contributions sampling the many diverse advancements that are currently ongoing in the MAS field.

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23

Liang, Hongtao, Fengju Kang, and Honghong Li. "UUV formation system modeling and simulation research based on Multi-Agent Interaction Chain." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 02 (May 29, 2015): 1550019. http://dx.doi.org/10.1142/s1793962315500191.

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Unmanned Underwater Vehicle (UUV) formation system has an important role in the utilization of marine resource. In order to provide an efficient method to research modeling and simulation of UUV formation in the marine environment, the novel approach based on Multi-Agent Interaction Chain was proposed for the UUV formation system. Firstly, Multi-Agent Interaction Chain was analyzed, which mainly considered task and role of UUV in the formation, and the overall modeling process of UUV formation system based on Multi-Agent Interaction Chain was established. Then, the static structure of Multi-Agent Interaction Chain was researched focusing on Hybrid UUV-Agent model structure from the UUV-Agent State-Set and UUV-Agent Rule-Base which were the two aspects to strengthen reliability of interaction chain; the dynamic mechanism of Multi-Agent Interaction Chain was designed, which was focused on collaboration model and communication model through the Adaptive Dynamic Contract Net Protocol and KQML/XML/RTI. Finally, three experiments were established to verify the validity and effectiveness of proposed modeling approach for UUV formation system. Simulation results show the proposed model has good performance, which has important theoretical innovation and application prospects.

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24

Kartvelishvili, V. M., and Е. A. Lebedyuk. "THE MODEL OF AGENT AND MULTI-AGENT INTERACTION IN SOCIO-ECONOMIC SYSTEMS." Vestnik of the Plekhanov Russian University of Economics, no. 3 (June 10, 2018): 147–65. http://dx.doi.org/10.21686/2413-2829-2018-3-147-165.

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25

Cliff, Oliver M., Joseph T. Lizier, X. Rosalind Wang, Peter Wang, Oliver Obst, and Mikhail Prokopenko. "Quantifying Long-Range Interactions and Coherent Structure in Multi-Agent Dynamics." Artificial Life 23, no. 1 (February 2017): 34–57. http://dx.doi.org/10.1162/artl_a_00221.

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We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches.

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26

ALONSO, EDUARDO, MARK D'INVERNO, DANIEL KUDENKO, MICHAEL LUCK, and JASON NOBLE. "Learning in multi-agent systems." Knowledge Engineering Review 16, no. 3 (September 2001): 277–84. http://dx.doi.org/10.1017/s0269888901000170.

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In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

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27

Benoudina, Lazhar, and Mohammed RedjimiRedjimi. "Multi Agent System Based Approach for Industrial Process Simulation." Journal Européen des Systèmes Automatisés 54, no. 2 (April 27, 2021): 209–17. http://dx.doi.org/10.18280/jesa.540202.

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Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.

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de Hauwere, Yann-Michaël, Sam Devlin, Daniel Kudenko, and Ann Nowé. "Context-sensitive reward shaping for sparse interaction multi-agent systems." Knowledge Engineering Review 31, no. 1 (January 2016): 59–76. http://dx.doi.org/10.1017/s0269888915000193.

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AbstractPotential-based reward shaping is a commonly used approach in reinforcement learning to direct exploration based on prior knowledge. Both in single and multi-agent settings this technique speeds up learning without losing any theoretical convergence guarantees. However, if speed ups through reward shaping are to be achieved in multi-agent environments, a different shaping signal should be used for each context in which agents have a different subgoal or when agents are involved in a different interaction situation.This paper describes the use of context-aware potential functions in a multi-agent system in which the interactions between agents are sparse. This means that, unknown to the agentsa priori, the interactions between the agents only occur sporadically in certain regions of the state space. During these interactions, agents need to coordinate in order to reach the global optimal solution.We demonstrate how different reward shaping functions can be used on top of Future Coordinating Q-learning (FCQ-learning); an algorithm capable of automatically detecting when agents should take each other into consideration. Using FCQ-learning, coordination problems can even be anticipated before the actual problems occur, allowing the problems to be solved timely. We evaluate our approach on a range of gridworld problems, as well as a simulation of air traffic control.

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29

Zhang, Yun, and Yan Lian Zhang. "Warehousing Management Based on Multi-Agent." Key Engineering Materials 467-469 (February 2011): 1598–603. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1598.

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This paper introduced Agent technology in the warehousing management in China, by taking advantage of its characteristics such as autonomy, reactivity, sociality and so on, and defining the interaction and cooperation mechanism between different Agent to achieve seamless connection between enterprises, so as to reduce or even to eliminate the inventory, supply viable idea and method of warehousing management for enterprises.

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Jha, Kunal, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, and Tianmin Shu. "Neural Amortized Inference for Nested Multi-Agent Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 530–37. http://dx.doi.org/10.1609/aaai.v38i1.27808.

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Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.

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31

Cardoso, Rafael C., and Angelo Ferrando. "A Review of Agent-Based Programming for Multi-Agent Systems." Computers 10, no. 2 (January 27, 2021): 16. http://dx.doi.org/10.3390/computers10020016.

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Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.

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32

Hai-long, Liu, and Wu Tie-jun. "Analysis of the KQML model in multi-agent interaction." Journal of Zhejiang University-SCIENCE A 2, no. 2 (April 2001): 132–36. http://dx.doi.org/10.1631/jzus.2001.0132.

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33

KATAGAMI, Daisuke, Hidefumi OHMURA, Yoshiaki YASUMURA, and Katsumi NITTA. "Multi User Learning Agent (MULA) based on Social Interaction." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 17, no. 3 (2005): 340–50. http://dx.doi.org/10.3156/jsoft.17.340.

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34

Qu, Zhi Jian, Liang Guo, Hong Ping Ling, Ge Chen, and Li Liu. "Multi-Agent Information Interaction Research for Distribution Dispatch Monitoring." Advanced Materials Research 791-793 (September 2013): 962–66. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.962.

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In allusion to the transmission difficult difficulties problem of massive monitoring information flow, due to numerous on-line processing points and quick variation of operating parameters in distribution network monitoring dispatching, an new asynchronous processing method for batch information based on multi-agent alliance technology is proposed. Multi-agent alliance Platform is constructed by means of designing JACK software. Then using CIM-mapping technology and news event asynchronous trigger technology, massive data interactive real-time processing is implemented. Taking the monitoring system for 10kV railway distribution network as example, the synchronous interaction and performance tests are carried out for 10000 analog quantities and state quantity measurement data, the transmission interaction processing time is 582.08ms.

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35

Schmitt, Gerhard, Maia Engeli, David Kurmann, Boi Faltings, and Stefan Monier. "Multi-agent interaction in a complex virtual design environment." AI Communications 9, no. 2 (1996): 74–78. http://dx.doi.org/10.3233/aic-1996-9206.

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36

Chen, Zhifu, Huimin Liao, and Tianguang Chu. "Clustering in multi-agent swarms via medium-range interaction." EPL (Europhysics Letters) 96, no. 4 (November 1, 2011): 40015. http://dx.doi.org/10.1209/0295-5075/96/40015.

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37

Shevchenko, V. I., A. V. Skatkov, A. A. Bryukhovetskiy, O. V. Chengar, and T. A. Kokodey. "Multi-agent model of information interaction among unmanned vehicles." Journal of Physics: Conference Series 1515 (April 2020): 022039. http://dx.doi.org/10.1088/1742-6596/1515/2/022039.

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38

Sun, Yinshuang, Zhijian Ji, Qingyuan Qi, and Huizi Ma. "Bipartite Consensus of Multi-Agent Systems With Intermittent Interaction." IEEE Access 7 (2019): 130300–130311. http://dx.doi.org/10.1109/access.2019.2940541.

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39

LOW, CHI KEEN, RALPH RÖNNQUIST, and TSONG YUEH CHEN. "AN AUTOMATED TOOL (IDAF) TO MANIPULATE INTERACTION DIAGRAMS AND FRAGMENTATIONS FOR MULTI-AGENT SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 09, no. 01 (February 1999): 127–49. http://dx.doi.org/10.1142/s0218194099000085.

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Interaction diagrams are used in multi-agent systems to graphically describe agent computation threads and communications while fragmentations are the algebraic representations of interaction diagrams. The IDAF (Interaction Diagrams And Fragmentations) tool suite has been developed based on the formalism of interaction diagrams and fragmentations. The tool suite consists of ValidatoR, FormatteR, TranslatoR, GrapheR and TesteR. This paper describes the usage of the tool suite and demonstrates it in two different multi-agent systems.

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Kim, Jonghoek. "Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction." Sensors 23, no. 19 (September 23, 2023): 8050. http://dx.doi.org/10.3390/s23198050.

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This paper considers a multi-agent foraging problem, where multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location, called the base. This article considers the case where autonomous agents move in unknown 3-D workspace with many obstacles. This article describes 3-D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. This paper proposes a 3-D foraging strategy which has the following two steps. The first step is to detect all pucks inside the 3-D cluttered unknown workspace, such that every puck in the workspace is detected in a provably complete manner. The next step is to generate a path from the base to every puck, followed by collecting every puck to the base. Since an agent cannot use global localization, each agent depends on local interaction to bring every puck to the base. In this article, every agent on a path to a puck is used for guiding an agent to reach the puck and to bring the puck to the base. To the best of our knowledge, this article is novel in letting multiple agents perform foraging and puck carrying in 3-D cluttered unknown workspace, while not relying on global localization of an agent. In addition, the proposed search strategy is provably complete in detecting all pucks in the 3-D cluttered bounded workspace. MATLAB simulations demonstrate the outperformance of the proposed multi-agent foraging strategy in 3-D cluttered workspace.

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Telnov, Yu F., A. V. Danilov, R. I. Diveev, V. A. Kazakov, and E. V. Yaroshenko. "Development of a prototype of multi-agent system of network interaction of educational institutions." Open Education 22, no. 6 (January 14, 2019): 14–26. http://dx.doi.org/10.21686/1818-4243-2018-6-14-26.

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The aim of the researchis to develop a prototype of the intelligent multi-agent system for dynamic interaction of the intelligent agents in the integrated information and educational space to solve the problem of formation of joint educational programs by several educational institutions.Materials and methods.In modern conditions of digital transformation of education the organization of network training of students on dynamically formed educational programs in accordance with the needs of the labor market and the individual requirements of students is becoming increasingly important. It is proposed to develop a software platform based on intelligent multi-agent technology for flexible integration of educational resources and implementation of joint educational programs by several interacting educational institutions. As a basis for the development of the software prototype architecture, the specifications of the developer community for the standardization of agent technologies FIPA (the Foundation for Intelligent Physical Agents), and the software tool environment – JADE framework (Java Agent Development Network) were chosen.Results.The paper presents the architecture of intelligent multi-agent system for network interaction of educational institutions in the integrated information and educational space, which allows to dynamically forming educational programs in accordance with the requested professional competencies. The structure of the ontology of information and educational space, providing the interaction of intelligent agents, is justified, and the mechanism of its display from the OWL format to the format of the tool environment JADE, using the plugin Protege is described. The description of the software prototype, the structure of intelligent agents in the JADE format and the technology of agent interaction, based on the FIPA protocols in the process of educational programs formation is presented.Conclusion.The implementation of the multi-agent system prototype for network interaction of educational institutions allows you to quickly create educational programs in accordance with individual and group learning trajectories under the specific formed professional competence. The presented software prototype with some modification can be used for other subject areas of the digital economy, involving the dynamic formation of network structures of interaction for business partners.

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Lavendelis, Egons, and Janis Grundspenkis. "Design of Multi-Agent Based Intelligent Tutoring Systems." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 48–59. http://dx.doi.org/10.2478/v10143-009-0004-z.

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Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.

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Sun, Xuelong, Cheng Hu, Tian Liu, Shigang Yue, Jigen Peng, and Qinbing Fu. "Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics." Biomimetics 8, no. 8 (December 1, 2023): 580. http://dx.doi.org/10.3390/biomimetics8080580.

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Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

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Lee, Yugyung, and Quddus Chong. "Multi-agent systems support for Community-Based Learning." Interacting with Computers 15, no. 1 (January 2003): 33–55. http://dx.doi.org/10.1016/s0953-5438(02)00057-7.

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Mariani, Stefano, and Andrea Omicini. "Special Issue “Multi-Agent Systems”: Editorial." Applied Sciences 9, no. 5 (March 6, 2019): 954. http://dx.doi.org/10.3390/app9050954.

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Multi-agent systems (MAS) allow and promote the development of distributed and intelligent applications in complex and dynamic environments. Applications of this kind have a crucial role in our everyday life, as witnessed by the broad range of domains they are deployed to—such as manufacturing, management sciences, e-commerce, biotechnology, etc. Despite heterogeneity, those domains share common requirements such as autonomy, structured interaction, mobility, and openness—which are well suited for MAS. Therein, in fact, goal-oriented processes can enter and leave the system dynamically and interact with each other according to structured protocols. This special issue gathers 17 contributions spanning from agent-based modelling and simulation to applications of MAS in situated and socio-technical systems.

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Aman, Bogdan, and Gabriel Ciobanu. "Knowledge Dynamics and Behavioural Equivalences in Multi-Agent Systems." Mathematics 9, no. 22 (November 11, 2021): 2869. http://dx.doi.org/10.3390/math9222869.

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We define a process calculus to describe multi-agent systems with timeouts for communication and mobility able to handle knowledge. The knowledge of an agent is represented as sets of trees whose nodes carry information; it is used to decide the interactions with other agents. The evolution of the system with exchanges of knowledge between agents is presented by the operational semantics, capturing the concurrent executions by a multiset of actions in a labelled transition system. Several results concerning the relationship between the agents and their knowledge are presented. We introduce and study some specific behavioural equivalences in multi-agent systems, including a knowledge equivalence able to distinguish two systems based on the interaction of the agents with their local knowledge.

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Ma, Lizhu, and Xin Zhang. "Hierarchical Social Network Analysis Using a Multi-Agent System." International Journal of Agent Technologies and Systems 5, no. 3 (July 2013): 14–32. http://dx.doi.org/10.4018/ijats.2013070102.

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The quality of K-12 education has been a major concern in the nation for years. School systems, just like many other social networks, appear to have a hierarchical structure. Understanding this structure could be the key to better evaluating student performance and improving school quality. Many studies have been focusing on detecting hierarchical structure by using hierarchical clustering algorithms. The authors design an interaction-based similarity measure to accomplish hierarchical clustering in order to detect hierarchical structures in social networks (e.g. school district networks). This method uses a multi-agent system, for it is based on agent interactions. With the network structure detected, they also built a model, which is based on the MAXQ algorithm, to decompose the funding policy task into subtasks and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. For the experiment, the authors used real school data from Bexar county’s 15 school districts in Texas. The first result shows that their interaction-based method is able to generate meaningful clustering and dendrograms for social networks. Additionally the authors’ policy evaluation model is able to evaluate funding policies from the past three years in Bexar County and conclude that increasing funding does not necessarily have a positive impact on student performance and it is generally not the case that the more is spent, the better.

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Roest, G. B., and N. B. Szirbik. "Escape and intervention in multi-agent systems." AI & SOCIETY 24, no. 1 (February 19, 2009): 25–34. http://dx.doi.org/10.1007/s00146-009-0193-6.

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Brazier, Frances M. T., Barbara M. Dunin-Keplicz, Nick R. Jennings, and Jan Treur. "Desire: Modelling Multi-Agent Systems in a Compositional Formal Framework." International Journal of Cooperative Information Systems 06, no. 01 (March 1997): 67–94. http://dx.doi.org/10.1142/s0218843097000069.

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This paper discusses an example of the application of a high-level modelling framework which supports both the specification and implementation of a system's conceptual design. This framework, DESIRE (framework for DEsign and Specification of Interacting REasoning components), explicitly models the knowledge, interaction, and coordination of complex tasks and reasoning capabilities in agent systems. For the application domain addressed in this paper, an operational multi-agent system which manages an electricity transportation network for a Spanish electricity utility, a comprehensible specification is presented.

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Ruan, Jingqing, Linghui Meng, Xuantang Xiong, Dengpeng Xing, and Bo Xu. "Learning Multi-Agent Action Coordination via Electing First-Move Agent." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 624–28. http://dx.doi.org/10.1609/icaps.v32i1.19850.

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Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously, and asynchronous action coordination between agents is rarely considered. This paper introduces a bi-level multi-agent decision hierarchy for coordinated behavior planning. We propose a novel election mechanism in which we adopt a graph convolutional network to model the interaction among agents and elect a first-move agent for asynchronous guidance. We also propose a dynamically weighted mixing network to effectively reduce the misestimation of the value function during training. This work is the first to explicitly model the asynchronous multi-agent action coordination, and this explicitness enables to choose the optimal first-move agent. The results on Cooperative Navigation and Google Football demonstrate that the proposed algorithm can achieve superior performance in cooperative environments. Our code is available at https://github.com/Amanda-1997/EFA-DWM.

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