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Artículos de revistas sobre el tema "Interaction multi-agents"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

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

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2

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 (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

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3

Bucher, Andreas, Mateusz Dolata, Sven Eckhardt, Dario Staehelin, and Gerhard Schwabe. "Talking to Multi-Party Conversational Agents in Advisory Services: Command-based vs. Conversational Interactions." Proceedings of the ACM on Human-Computer Interaction 8, GROUP (2024): 1–25. http://dx.doi.org/10.1145/3633072.

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Interacting with a conversational agent (CA) is becoming a major paradigm for human-technology interaction. Yet, ways for interacting with CAs are still forming, especially in situations involving more than one human. Starting an interaction with a CA might involve a wakeword and command. Alternatively, it could become active based on implicit requests and context information. Hence, CA designers face a serious dilemma: explicit commands disturb a natural conversation flow, while implicit requests might cause inadequate CA behavior. This study explores this dilemma and discusses observations f

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4

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 (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 sys

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5

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 (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 m

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6

Emelyanov, Viktor V. "Organization of the Agents Interaction in Multi-Agents of Production Coordination System." IFAC Proceedings Volumes 33, no. 17 (2000): 485–89. http://dx.doi.org/10.1016/s1474-6670(17)39450-8.

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7

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 &ndash; 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

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8

ZHANG, Kun, Yoichiro MAEDA, and Yasutake TAKAHASHI. "Learning Model Considering the Interaction among Heterogeneous Multi-Agents." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 24, no. 5 (2012): 1002–11. http://dx.doi.org/10.3156/jsoft.24.1002.

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9

Zhang, Kun, Yoichiro Maeda, and Yasutake Takahashi. "Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (2011): 896–903. http://dx.doi.org/10.20965/jaciii.2011.p0896.

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Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward r

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10

Jin, Kun, Yevgeniy Vorobeychik, and Mingyan Liu. "Multi-Scale Games: Representing and Solving Games on Networks with Group Structure." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5497–505. http://dx.doi.org/10.1609/aaai.v35i6.16692.

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Network games provide a natural machinery to compactly represent strategic interactions among agents whose payoffs exhibit sparsity in their dependence on the actions of others. Besides encoding interaction sparsity, however, real networks often exhibit a multi-scale structure, in which agents can be grouped into communities, those communities further grouped, and so on, and where interactions among such groups may also exhibit sparsity. We present a general model of multi-scale network games that encodes such multi-level structure. We then develop several algorithmic approaches that leverage

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11

Benoudina, Lazhar, and Mohammed RedjimiRedjimi. "Multi Agent System Based Approach for Industrial Process Simulation." Journal Européen des Systèmes Automatisés 54, no. 2 (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 in

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12

Penner, Robin R. "Multi-Agent Societies for Collaborative Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 15 (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

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13

Jiang, Min, Zhiqing Meng, Xinsheng Xu, Rui Shen, and Gengui Zhou. "Multiobjective Interaction Programming Problem with Interaction Constraint for Two Players." Mathematical Problems in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/618928.

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This paper extends an existing cooperative multi-objective interaction programming problem with interaction constraint for two players (or two agents). First, we define ans-optimal joint solution with weight vector to multi-objective interaction programming problem with interaction constraint for two players and get some properties of it. It is proved that thes-optimal joint solution with weight vector to the multi-objective interaction programming problem can be obtained by solving a corresponding mathematical programming problem. Then, we define anothers-optimal joint solution with weight va

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14

Schmidt, Susanne, Oscar Ariza, and Frank Steinicke. "Intelligent Blended Agents: Reality–Virtuality Interaction with Artificially Intelligent Embodied Virtual Humans." Multimodal Technologies and Interaction 4, no. 4 (2020): 85. http://dx.doi.org/10.3390/mti4040085.

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Intelligent virtual agents (VAs) already support us in a variety of everyday tasks such as setting up appointments, monitoring our fitness, and organizing messages. Adding a humanoid body representation to these mostly voice-based VAs has enormous potential to enrich the human–agent communication process but, at the same time, raises expectations regarding the agent’s social, spatial, and intelligent behavior. Embodied VAs may be perceived as less human-like if they, for example, do not return eye contact, or do not show a plausible collision behavior with the physical surroundings. In this ar

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15

Aman, Bogdan, and Gabriel Ciobanu. "Knowledge Dynamics and Behavioural Equivalences in Multi-Agent Systems." Mathematics 9, no. 22 (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

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16

Ababii, Victor, Viorica Sudacevschi, Silvia Munteanu, Ana Turcan, and Olesea Borozan. "Decision-Making Support System for Quality Smart City Services." International Journal of Progressive Sciences and Technologies 39, no. 1 (2023): 450. http://dx.doi.org/10.52155/ijpsat.v39.1.5436.

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This paper presents the results of research carried out in the field of developing decision support systems for quality Smart City services. The decision-making system consists of two sets of Agents: Service Provider Agents and Service Consumer Agents. The interaction between the Agent sets is governed by the knowledge base which is managed by the Service Quality Assessors. Service quality is evaluated based on a Multi-Objective Optimization model competitively performed by applying game theory (Nash Equilibrium) between Agent sets involving available resources and knowledge. The paper develop

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17

Serrano, Emilio, and Javier Bajo. "Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning." Sensors 20, no. 18 (2020): 5198. http://dx.doi.org/10.3390/s20185198.

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The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ int

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18

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 (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

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19

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 (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 pr

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20

Xu, Tian, Hui Zhang, and Chen Yu. "Cooperative gazing behaviors in human multi-robot interaction." Interaction Studies 14, no. 3 (2013): 390–418. http://dx.doi.org/10.1075/is.14.3.05xu.

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When humans are addressing multiple robots with informative speech acts (Clark & Carlson 1982), their cognitive resources are shared between all the participating robot agents. For each moment, the user’s behavior is not only determined by the actions of the robot that they are directly gazing at, but also shaped by the behaviors from all the other robots in the shared environment. We define cooperative behavior as the action performed by the robots that are not capturing the user’s direct attention. In this paper, we are interested in how the human participants adjust and coordinate their

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21

Yesikova, Tatyana, and Svetlana Vakhrusheva. "ASSESSMENT OF CONSEQUENCES OF IMPLEMENTATION OF LARGE-SCALE INFRASTRUCTURE PROJECTS BASED ON THE AGENT APPROACH: TOPOLOGY OF THE MULTIAGENT SYSTEM." Interexpo GEO-Siberia 3, no. 1 (2019): 109–16. http://dx.doi.org/10.33764/2618-981x-2019-3-1-109-116.

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The article poses the problem of modeling processes associated with the construction of large-scale infrastructural projects in the context of the Bering Strait tunnel (road construction in the Far North). The purpose of the simulation is to identify potential problems and estimate losses for various participants in similar projects. The study is based on such a simulation method as multi-agent modeling. The article describes the basics of building the topology of a multi-agent system in relation to this task: decomposing a process into subprocesses, identifying the main active agents, describ

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22

Li, Jing, and Yue Jin Zhou. "Simulation of Conflicts Resolution in Virtual Teams." Advanced Materials Research 187 (February 2011): 39–44. http://dx.doi.org/10.4028/www.scientific.net/amr.187.39.

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The purpose of the paper is to study the conflict resolution in virtual teams. Multi-agent technology is used to simulate the virtual team. In the team, agents adapt the Q-learning algorithm to adjust their behaviors. Through the interaction of virtual members, part of conflicts can be resolved by team members. The experiments are manipulated to study the process of the interaction in the team. The results of experiments show a new rule for conflict resolution emerged from the dynamic interactions of agents. The conclusions show significance on the management of team in real world.

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23

Li, Tongyue, Dianxi Shi, Songchang Jin, Zhen Wang, Huanhuan Yang, and Yang Chen. "Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning." Entropy 27, no. 1 (2024): 4. https://doi.org/10.3390/e27010004.

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Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor–critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments. Specifically,

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24

Olaru, Andrei, and Monica Pricope. "Multi-Modal Decentralized Interaction in Multi-Entity Systems." Sensors 23, no. 6 (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 u

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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 (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

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26

Calliess, Jan-P., and Stephen Roberts. "Multi-Agent Planning with Mixed-Integer Programming and Adaptive Interaction Constraint Generation (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (2021): 207–8. http://dx.doi.org/10.1609/socs.v4i1.18304.

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We consider multi-agent planning in which the agents' optimal plans are solutions to mixed-integer programs (MIP) that are coupled via integer constraints. While in principle, one could find the joint solution by combining the separate problems into one large joint centralized MIP, this approach rapidly becomes intractable for growing numbers of agents and large problem domains. To address this issue, we propose an iterative approach that combines conflict detection with constraint-generation whereby the agents plan repeatedly until all conflicts are resolved. In each planning iteration, the a

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27

Wang, Haixing, Yi Yang, Zhiwei Lin, and Tian Wang. "Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood." Actuators 11, no. 4 (2022): 99. http://dx.doi.org/10.3390/act11040099.

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In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a multi-agent system, in order to obtain the optimal decision from the agents. In the new Q-value function, the OEAN is used to depict the equivalent interaction between the current agent and the others. To deal with the non-stationary environment when agents act, the OEAN of the current agent is inferred simultaneously by the maximum

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28

Dubenko, Yu V. "ANALYTICAL REVIEW OF MULTI-AGENT REINFORCEMENT LEARNING PROBLEMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 192 (June 2020): 48–56. http://dx.doi.org/10.14489/vkit.2020.06.pp.048-056.

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This paper is devoted to the problem of collective artificial intelligence in solving problems by intelligent agents in external environments. The environments may be: fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous. The paper identifies problems of collective interaction of intelligent agents when they solve a class of tasks, which need to coordinate actions of agent group, e. g. task of exploring the territory of a complex infrastructure facility. It is revealed that the problem of reinforcement training in multi-agent systems is poorly p

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Dubenko, Yu V. "ANALYTICAL REVIEW OF MULTI-AGENT REINFORCEMENT LEARNING PROBLEMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 192 (June 2020): 48–56. http://dx.doi.org/10.14489/vkit.2020.06.pp.048-056.

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This paper is devoted to the problem of collective artificial intelligence in solving problems by intelligent agents in external environments. The environments may be: fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous. The paper identifies problems of collective interaction of intelligent agents when they solve a class of tasks, which need to coordinate actions of agent group, e. g. task of exploring the territory of a complex infrastructure facility. It is revealed that the problem of reinforcement training in multi-agent systems is poorly p

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30

Bertaglia, Giulia, Lorenzo Pareschi, and Giuseppe Toscani. "Modelling contagious viral dynamics: a kinetic approach based on mutual utility." Mathematical Biosciences and Engineering 21, no. 3 (2024): 4241–68. http://dx.doi.org/10.3934/mbe.2024187.

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<abstract><p>The temporal evolution of a contagious viral disease is modelled as the dynamic progression of different classes of population with individuals interacting pairwise. This interaction follows a binary mechanism typical of kinetic theory, wherein agents aim to improve their condition with respect to a mutual utility target. To this end, we introduce kinetic equations of Boltzmann-type to describe the time evolution of the probability distributions of the multi-agent system. The interactions between agents are defined using principles from price theory, specifically emplo

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31

KONING, JEAN-LUC. "A REVIEW ON THE INTERACTION ISSUES IN AGENT-BASED MARKETPLACES." International Journal of Information Technology & Decision Making 01, no. 03 (2002): 457–71. http://dx.doi.org/10.1142/s0219622002000294.

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While there are already literature surveys upon agent-mediated electronic commerce applications, none have specifically tackled the issue from an interaction perspective or looked at how the control is distributed among the agents. This state-of-the-art survey focuses on how agent interactions are handled. First, it deeply looks at how methods for enforcing the actions taken by agents have been dealt with, namely protocols, negotiation and auction. Second, it defines the various types of communication languages used in multi-agent market architectures. The three main alternatives are KQML, ACL

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32

Cui, Zhoujuan, Wenshuo Peng, Yaqiang Zhang, Yiping Duan, and Xiaoming Tao. "Spatio-Temporal-Interaction Graph Neural Networks for Multi-Agent Trajectory Prediction." Journal of Physics: Conference Series 2833, no. 1 (2024): 012010. http://dx.doi.org/10.1088/1742-6596/2833/1/012010.

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Abstract For intelligent transportation systems, accurately forecasting the future trajectories of multiple agents is pivotal. Considering the increased diversity of agents within a scene, in order to capture and model the variations in their appearance, motion status, behavioral patterns, and interrelationships, we propose a simple yet effective framework based on Spatio-Temporal-Interaction Graph Neural Networks. Specifically, a Multi-Class Agent Encoder is meticulously tailored to the specific class of each agent to distill pertinent information from their motion attributes and historical t

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Skanda, Suresh, Sridhar Sudarshan, B. Raj Supreeth, Mittalkod Purmina, and V. Sukhateertha. "Prism: A Multi-Agent System for Real-Time Multi- Modal Interaction in Mobile and Web Applications." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2024): 811–17. https://doi.org/10.5281/zenodo.14546550.

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The integration of multi-agent systems in mobile  and web applications has opened new horizons for real- time multi- modal interaction. This paper presents a  comprehensive exploration of a multi-agent framework leveraging the Qwen2.5:3B and Gemini 1.5 Flash 8B models to provide robust, scalable, and user-centric solutions. Agents for diverse functionalities—such as Cooking, Notes, Entertainment, Travel Planning, Weather, and SecureFace—are seamlessly integrated into a unified platform to address real-world challenges. The  framework emphasizes dynamic adaptability,

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Gautier, Anna, Bruno Lacerda, Nick Hawes, and Michael Wooldridge. "Multi-Unit Auctions for Allocating Chance-Constrained Resources." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11560–68. http://dx.doi.org/10.1609/aaai.v37i10.26366.

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Sharing scarce resources is a key challenge in multi-agent interaction, especially when individual agents are uncertain about their future consumption. We present a new auction mechanism for preallocating multi-unit resources among agents, while limiting the chance of resource violations. By planning for a chance constraint, we strike a balance between worst-case approaches, which under-utilise resources, and expected-case approaches, which lack formal guarantees. We also present an algorithm that allows agents to generate bids via multi-objective reasoning, which are then submitted to the auc

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35

Kim, Jonghoek. "Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction." Sensors 23, no. 19 (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

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Zhang, Kun, Yoichiro Maeda, and Yasutake Takahashi. "Cooperative Behavior Learning Based on Social Interaction of State Conversion and Reward Exchange Among Multi-Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 5 (2011): 606–16. http://dx.doi.org/10.20965/jaciii.2011.p0606.

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In multi-agent systems, it is necessary for autonomous agents to interact with each other in order to have excellent cooperative performance. Therefore, we have studied social interaction between agents to see how they acquire cooperative behavior. We have found that sharing environmental states can improve agent cooperation through reinforcement learning, and that changing environmental states to target-related individual states improves cooperation. To further improve cooperation, we propose reward redistribution based on reward exchanges among agents. In receiving rewards from both the envi

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Seabra, Antony, Claudio Cavalcante, Joao Nepomuceno, Lucas Lago, Nicolaas Ruberg, and Sergio Lifschitz. "Orchestrating Multi-Agent Systems for Multi-Source Information Retrieval and Question Answering with Large Language Models." International Journal on Natural Language Computing 13, no. 5/6 (2024): 27–46. https://doi.org/10.5121/ijnlc.2024.13603.

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We present a novel framework for developing robust multi-source questionanswer systems by dynamically integrating Large Language Models with diverse data sources. This framework leverages a multi-agent architecture to coordinate the retrieval and synthesis of information from unstructured documents, like PDFs, and structured databases. Specialized agents, including SQL agents, Retrieval-Augmented Generation agents, and router agents, dynamically select and execute the most suitable retrieval strategies for each query. To enhance contextual relevance and accuracy, the framework employs adaptive

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Araújo, Tanya, and Francisco Louçã. "Modeling a Multi-Agents System as a Network." International Journal of Agent Technologies and Systems 1, no. 4 (2009): 17–29. http://dx.doi.org/10.4018/jats.2009100102.

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The article presents an empirically oriented investigation on the dynamics of a specific case of a multi-agents system, the stock market. It demonstrates that S&P500 market space can be described using the geometrical and topological characteristics of its dynamics. The authors proposed to measure the coefficient R, an index providing information on the evolution of a manifold describing the dynamics of the market. It indicates the moments of perturbations, proving that the dynamics is driven by shocks and by a structural change. This dynamics has a characteristic dimension, which also all

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Iqbal, Muhammad Munwar, Yasir Saleem, Kashif Naseer, and Mucheol Kim. "Multimedia based student-teacher smart interaction framework using multi-agents in eLearning." Multimedia Tools and Applications 77, no. 4 (2017): 5003–26. http://dx.doi.org/10.1007/s11042-017-4615-z.

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Karamysheva, N. S., and S. A. Zinkin. "Interaction of cognitive and reactive agents in an intelligent computing system: operational semantics." Proceedings of the Southwest State University 28, no. 4 (2025): 138–53. https://doi.org/10.21869/2223-1560-2024-28-4-138-153.

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Purpose of research. The aim of this work is to develop an approach to constructing intelligent systems based on a replenished semantic network and a multi-agent environment with agents of various types: cognitive and deductive reactive. The architecture of a multi-level intelligent system is proposed and substantiated, which uses cognitive and reactive intelligent agents that differ in composition and number of implemented cognitive and deductive presumptions. Methods. Knowledge about the subject area is formalized both using the modal version of the first-order predicate calculus for describ

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Zhao, Zhitong, Ya Zhang, Siying Wang, Yang Zhou, Ruoning Zhang, and Wenyu Chen. "Assisted-Value Factorization with Latent Interaction in Cooperate Multi-Agent Reinforcement Learning." Mathematics 13, no. 9 (2025): 1429. https://doi.org/10.3390/math13091429.

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With the development of value decomposition methods, multi-agent reinforcement learning (MARL) has made significant progress in balancing autonomous decision making with collective cooperation. However, the collaborative dynamics among agents are continuously changing. The current value decomposition methods struggle to adeptly handle these dynamic changes, thereby impairing the effectiveness of cooperative policies. In this paper, we introduce the concept of latent interaction, upon which an innovative method for generating weights is developed. The proposed method derives weights from the hi

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42

Adam, Emmanuel, Martial Razakatiana, René Mandiau, and Christophe Kolski. "Matrices Based on Descriptors for Analyzing the Interactions between Agents and Humans." Information 14, no. 6 (2023): 313. http://dx.doi.org/10.3390/info14060313.

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The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It is then necessary to build models analyzing their decisions when interacting, while taking into account the specificities of these interactions. This paper, therefore, aimed to propose an explicit model of such interactions based on game theory, taking into account, not only environmental characteristics (

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Dubenko, Yu V. "AN ALGORITHM OF THE COLLECTIVE INTERACTION OF INTELLIGENT AGENTS IN CENTRALIZED MULTI-AGENT SYSTEMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 220 (October 2022): 30–42. http://dx.doi.org/10.14489/vkit.2022.10.pp.030-042.

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A centralized multiagent system based on the methods of feudal reinforcement learning, including agents-managers and agents-subordinates, is considered. A brief review of the author’s previous works on this topic is given. The standard algorithm for the functioning of systems of this type is considered, including the translation of the decision maker to agents-managers, the division of tasks by agents-managers into a set of subtasks, the choice by the agent-manager of the strategy used, the formation of reward functions by agents-managers, the assignment of tasks to agents-subordinates, the ex

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44

Oleksandr, Milov, Voitko Alexander, Husarova Iryna, et al. "DEVELOPMENT OF METHODOLOGY FOR MODELING THE INTERACTION OF ANTAGONISTIC AGENTS IN CYBERSECURITY SYSTEMS." Eastern-European Journal of Enterprise Technologies 2, no. 9 (98) (2019): 56–66. https://doi.org/10.15587/1729-4061.2019.164730.

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The basic concepts that form the basis of integrated modeling of the behavior of antagonistic agents in cybersecurity systems are identified. It is shown that the emphasis is largely on modeling the behavior of one of the cyber conflict parties only. In the case when the interaction of all parties to the conflict is considered, the approaches used are focused on solving particular problems, or they model a simplified situation. A methodology for modeling the interaction of antagonistic agents in cybersecurity systems, focused on the use of a multi-model complex with elements of cognitive model

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45

Cao, Hong, Rong Ma, Yanlong Zhai, and Jun Shen. "LLM-Collab: a framework for enhancing task planning via chain-of-thought and multi-agent collaboration." Applied Computing and Intelligence 4, no. 2 (2024): 328–48. https://doi.org/10.3934/aci.2024019.

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<p>Large language models have shown strong capabilities in performing natural language planning tasks, largely due to the chain-of-thought method, which enhances their ability to solve complex tasks through explicit intermediate inference. However, they face challenges in acquiring new knowledge, executing calculations, and interacting with the environment. Although previous work has enabled large language models to use external tools to improve reasoning and environmental interaction, there was no scalable or cohesive structure for these technologies. In this paper, we present LLM-Colla

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Cho, Seong-Sik, Sang-Ho Jo, Hyun-Jin Kim, et al. "Smoking may be more harmful to vasospastic angina patients who take antiplatelet agents due to the interaction: Results of Korean prospective multi-center cohort." PLOS ONE 16, no. 4 (2021): e0248386. http://dx.doi.org/10.1371/journal.pone.0248386.

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Background The interaction between smoking and the use of antiplatelet agents on the prognosis of vasospastic angina (VA) is rarely investigated. Methods VA-Korea is a nation-wide multi-center registry with prospective design (n = 1812). The primary endpoint was the composite occurrence of acute coronary syndrome (ACS), symptomatic arrhythmia, and cardiac death. Log-rank test and Cox proportional hazard model were for statistical analysis. Also, we conducted interaction analysis in both additive and multiplicative scales between smoking and antiplatelet agents among VA patients. For additive s

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Бредихин, А. В., Д. В. Веркошанский, Е. О. Неретин, and О. В. Собенина. "Intercomponent interaction in a multi-agent system." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 11, no. 3(42) (2023): 22–23. http://dx.doi.org/10.26102/2310-6018/2023.42.3.022.

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Статья посвящена изучению механизмов межкомпонентного взаимодействия в мультиагентных системах. В работе рассмотрены различные подходы к обмену сообщениями между компонентами, а также преимущества и недостатки каждого из них. Определены ключевые проблемы межкомпонентного взаимодействия и предложены их решения. Особое внимание уделено механизму обмена сообщениями на основе брокера сообщений. В статье описаны принципы работы программного брокера, его преимущества и недостатки, а также примеры использования в мультиагентных системах. Результаты исследования показали, что использование брокера соо

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Дубенко, Ю. В. "METHOD OF REUSE AND EXCHANGE OF EXPERIENCE IN THE COLLECTIVE INTERACTION OF INTELLIGENT AGENTS." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 1 (March 14, 2022): 62–72. http://dx.doi.org/10.36622/vstu.2022.18.1.007.

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Определены проблемы обмена и воспроизведения опыта, сгенерированного различными агентами, в задаче многоагентного обучения с подкреплением. Кратко рассмотрены другие работы автора статьи в области многоагентного обучения с подкреплением многоагентных систем, а также выводы из этих работ. Определено, что к числу проблем многоагентного обучения с подкреплением относятся проблемы обмена и воспроизведения опыта, сгенерированного различными агентами. Рассмотрена централизованная многоагентная система, основанная на принципах обучения с подкреплением. Описаны виды агентов, которые включает данная си

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Koochakzadeh, Abbasali, Mojtaba Naderi Soorki, Aydin Azizi, Kamran Mohammadsharifi, and Mohammadreza Riazat. "Delay-Dependent Stability Region for the Distributed Coordination of Delayed Fractional-Order Multi-Agent Systems." Mathematics 11, no. 5 (2023): 1267. http://dx.doi.org/10.3390/math11051267.

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Delay and especially delay in the transmission of agents’ information, is one of the most important causes of disruption to achieving consensus in a multi-agent system. This paper deals with achieving consensus in delayed fractional-order multi-agent systems (FOMAS). The aim in the present note is to find the exact maximum allowable delay in a FOMAS with non-uniform delay, i.e., the case in which the interactions between agents are subject to non-identical communication time-delays. By proving a stability theorem, the results available for non-delayed networked fractional-order systems are ext

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Randhavane, Tanmay, Aniket Bera, and Dinesh Manocha. "F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions." Presence: Teleoperators and Virtual Environments 26, no. 2 (2017): 228–46. http://dx.doi.org/10.1162/pres_a_00294.

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The simulation of human behaviors in virtual environments has many applications. In many of these applications, situations arise in which the user has a face-to-face interaction with a virtual agent. In this work, we present an approach for multi-agent navigation that facilitates a face-to-face interaction between a real user and a virtual agent that is part of a virtual crowd. In order to predict whether the real user is approaching a virtual agent to have a face-to-face interaction or not, we describe a model of approach behavior for virtual agents. We present a novel interaction velocity pr

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