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Journal articles on the topic 'Autonomous agents and multiagent systems'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Ur Rehman, Shafiq, and Aamer Nadeem. "An Approach to Model Based Testing of Multiagent Systems." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/925206.

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Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interac

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Endriss, Ulle, Ann Nowé, Maria Gini, et al. "Autonomous agents and multiagent systems." AI Matters 7, no. 3 (2021): 29–37. http://dx.doi.org/10.1145/3511322.3511329.

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The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.

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Dahlstedt, Palle, and Peter McBurney. "Musical Agents: Toward Computer-Aided Music Composition Using Autonomous Software Agents." Leonardo 39, no. 5 (2006): 469–70. http://dx.doi.org/10.1162/leon.2006.39.5.469.

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The authors, a composer and a computer scientist, discuss their collaborative research on the use of multiagent systems and their applicability to music and musical composition. They describe the development of software and techniques for the composition of generative music.

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Muñoz, Antonio, Pablo Anton, and Antonio Maña. "Multiagent Systems Protection." Advances in Software Engineering 2011 (August 15, 2011): 1–9. http://dx.doi.org/10.1155/2011/281517.

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Agent-systems can bring important benefits especially in applications scenarios where highly distributed, autonomous, intelligence, self-organizing, and robust systems are required. Furthermore, the high levels of autonomy and self-organizations of agent systems provide excellent support for developments of systems in which dependability is essential. Both Ubiquitous Computing and Ambient Intelligence scenarios belong in this category. Unfortunately, the lack of appropriate security mechanisms, both their enforcement and usability, is hindering the application of this paradigm in real-world ap

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Hyso, Alketa, and Eva Cipi. "Autonomous Agents as Tools for Modeling and Building Complex Control Systems that Operate in Dynamic and Unpredictable Environment." International Journal of Business & Technology 1, no. 2 (2013): 47–53. http://dx.doi.org/10.33107/ijbte.2013.1.2.05.

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Complex control systems that operate in not entirely predictable environment have to deal with this environment in an autonomous manner using adaptability, the ability to predict environmental changes, and to maintain their integrity. Elements of the system must be able to find a new solution in a dynamic way. In this paper, we present the modeling of a traffic lights’ control system using a multivalent system. This is a large-scale distributed system, consisting of autonomous and rational traffic light agents, in which there is no centre imposing an outcome. Multiagent system brings another k

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Silva, Felipe Leno Da, and Anna Helena Reali Costa. "A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems." Journal of Artificial Intelligence Research 64 (March 11, 2019): 645–703. http://dx.doi.org/10.1613/jair.1.11396.

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   Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem,

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Sonenberg, Liz, Peter Stone, Kagan Tumer, and Pinar Yolum. "Ten Years of AAMAS: Introduction to the Special Issue." AI Magazine 33, no. 3 (2012): 11. http://dx.doi.org/10.1609/aimag.v33i3.2423.

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Mackin, Kenneth James. "Autonomous Learning of Agent Communication and Group Behavior in Intelligent Multiagent Systems." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 2 (2003): 187. http://dx.doi.org/10.3156/jsoft.15.187_2.

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Panella, Alessandro. "Multiagent Stochastic Planning With Bayesian Policy Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1672–73. http://dx.doi.org/10.1609/aaai.v27i1.8506.

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When operating in stochastic, partially observable, multiagent settings, it is crucial to accurately predict the actions of other agents. In my thesis work, I propose methodologies for learning the policy of external agents from their observed behavior, in the form of finite state controllers. To perform this task, I adopt Bayesian learning algorithms based on nonparametric prior distributions, that provide the flexibility required to infer models of unknown complexity. These methods are to be embedded in decision making frameworks for autonomous planning in partially observable multiagent sys

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Satybaldiyeva, A., A. Ismailova, R. Moldasheva, A. Mukhanova, and K. Kadirkulov. "ABSTRACT DATA TYPES FOR KNOWLEDGE REPRESENTATION AND SPECIFICATION OF MULTI-AGENT SYSTEMS." PHYSICO-MATHEMATICAL SERIES 2, no. 336 (2021): 48–55. http://dx.doi.org/10.32014/2021.2518-1726.20.

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Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms

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Ali, G., N. A. Shaikh, A. W. Shaikh, and Z. A. Shaikh. "A MULTIAGENT SYSTEM BASED AUTOMATED PROFILING MODEL TO AVOID INSIDER THREAT." Nucleus 48, no. 2 (2011): 121–27. https://doi.org/10.71330/thenucleus.2011.847.

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The proposed model builds and maintains the profile of all insiders to detect and avoid threat. Software agents are autonomously working to record all activities of the authenticated users. The profile is compared with the policy of the organization and insiderâ€™s profile is marked acceptable or suspicious. This will lead to a proper mechanism to protect organizations assets against threat. The model is generic, adaptable and works in most of the organizations. It follows the renowned agent standard of Foundation for Intelligent Physical Agents (FIPA) as agents built on other platforms can in

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Wang, Sung-Jung, and S. K. Jason Chang. "Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning." Journal of Advanced Transportation 2021 (July 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/6654254.

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Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient a

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BOELLA, GUIDO, and LEENDERT VAN DER TORRE. "NORM NEGOTIATION IN MULTIAGENT SYSTEMS." International Journal of Cooperative Information Systems 16, no. 01 (2007): 97–122. http://dx.doi.org/10.1142/s0218843007001585.

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Normative multiagent systems provide agents with abilities to autonomously devise societies and organizations coordinating their behavior via social norms and laws. In this paper, we study how agents negotiate new social norms and when they accept them. We introduce a negotiation model based on what we call the social delegation cycle, which explains the negotiation of new social norms from agent desires in three steps. First, individual agents or their representatives negotiate social goals, then a social goal is negotiated in a social norm, and finally the social norm is accepted by the agen

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Gath, Max, Stefan Edelkamp, and Otthein Herzog. "Agent-Based Dispatching Enables Autonomous Groupage Traffic." Journal of Artificial Intelligence and Soft Computing Research 3, no. 1 (2013): 27–40. http://dx.doi.org/10.2478/jaiscr-2014-0003.

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Abstract The complexity and dynamics in groupage traffic require flexible, efficient, and adaptive planning and control processes. The general problem of allocating orders to vehicles can be mapped into the Vehicle Routing Problem (VRP). However, in practical applications additional requirements complicate the dispatching processes and require a proactive and reactive system behavior. To enable automated dispatching processes, this article presents a multiagent system where the decision making is shifted to autonomous, interacting, intelligent agents. Beside the communication protocols and the

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Sinha, A., and D. Ghose. "Control of Multiagent Systems Using Linear Cyclic Pursuit With Heterogenous Controller Gains." Journal of Dynamic Systems, Measurement, and Control 129, no. 5 (2006): 742–48. http://dx.doi.org/10.1115/1.2764505.

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In this paper, behavior of a group of autonomous mobile agents under cyclic pursuit is studied. Cyclic pursuit is a simple distributed control law, in which the agent i pursues agent i+1modn. The equations of motion are linear, with no kinematic constraints on motion. Behaviorally, they are identical but may have different controller gains. We generalize existing results in the literature, which consider only homogenous gains, to the case where controller gains are heterogenous. We show that, by selecting suitable controller gains, collective behavior of agents can be controlled significantly

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Doshi, Prashant J. "Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks." AI Magazine 33, no. 4 (2012): 82. http://dx.doi.org/10.1609/aimag.v33i4.2402.

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Decision making is a key feature of autonomous systems. It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also

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Iyer, Karthik, and Michael N. Huhns. "Negotiation criteria for multiagent resource allocation." Knowledge Engineering Review 24, no. 2 (2009): 111–35. http://dx.doi.org/10.1017/s0269888909000204.

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AbstractNegotiation in a multiagent system is a topic of active interest for enabling the allocation of scarce resources among autonomous agents. This paper presents a discussion of the research on negotiation criteria, which puts in context the contributions to resource allocation from the fields of economics, mathematics, and multiagent systems. We group the criteria based on how they relate to each other as well as their historical origin. In addition, we present three new criteria: verifiability, dimensionality, and topology. The criteria are organized into five categories. The allocation

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Dresner, K., and P. Stone. "A Multiagent Approach to Autonomous Intersection Management." Journal of Artificial Intelligence Research 31 (March 31, 2008): 591–656. http://dx.doi.org/10.1613/jair.2502.

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Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the drivi

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Orłowski, Mateusz, and Paweł Skruch. "Multiagent Manuvering with the Use of Reinforcement Learning." Electronics 12, no. 8 (2023): 1894. http://dx.doi.org/10.3390/electronics12081894.

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This paper presents an approach for defining, solving, and implementing dynamic cooperative maneuver problems in autonomous driving applications. The formulation of these problems considers a set of cooperating cars as part of a multiagent system. A reinforcement learning technique is applied to find a suboptimal policy. The key role in the presented approach is a multiagent maneuvering environment that allows for the simulation of car-like agents within an obstacle-constrained space. Each of the agents is tasked with reaching an individual goal, defined as a specific location in space. The po

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Bielecki, Andrzej, and Sylwia Nieszporska. "Analysis of Healthcare Systems by Using Systemic Approach." Complexity 2019 (April 21, 2019): 1–12. http://dx.doi.org/10.1155/2019/6807140.

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National healthcare systems in all countries do not act effectively. Therefore, especially strategies for introducing organizational innovation to public organization should be considered. The problem is how to organize the research in this field. One of the generally accepted solutions is the systemic approach to healthcare systems. In this paper multiagent systems theory and autonomous systems theory are applied to the analysis of main types of healthcare systems. Such analysis allows us to consider the system properties: the level of the autonomy, energy dissipation in the system, the payof

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Manjunatha, H. M., T. S. Karibasavaraju, Chandraiah Shadaksharaiah, and Kumar P. Arun. "An Intelligent Method to Load Management of Microgrid Operation using Multi-Agent System." Journal of Recent Trends in Electrical Power System 5, no. 2 (2022): 1–8. https://doi.org/10.5281/zenodo.7085287.

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<em>The future power systems will be featured by microgrid systems capable of integrating generation, load, and storage assets into an autonomous power system. In this paper the design and development of a multiagent system for the control of microgrid is presented. Then, fuzzy logic based technique is described for achieving priority based load management of a microgrid.</em>  

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Binmad, Ruchdee, and Mingchu Li. "Psychology-Inspired Trust Restoration Framework in Distributed Multiagent Systems." Scientific Programming 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/7515860.

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Trust violation during cooperation of autonomous agents in multiagent systems is usually unavoidable and can arise due to a wide number of reasons. From a psychological point of view, the violation of an agent’s trust is a result of one agent (which is a transgressor) expressing a very low weight on the welfare of another agent (which is a victim) by inflicting a high cost for a very small benefit. In order for the victim to make an effective decision about whether to cooperate or punish for the next interaction, a psychological variable called welfare tradeoff ratio (WTR) can be used to upreg

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Silva, Marco Antonio Gomes, Osvaldo Luis Asato, Percy Javier Igei Kaneshiro, and Francisco Yastami Nakamoto. "Architecture for Multiagent Systems with UNS Middleware and mediator agent using object-centric process mining." OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA 22, no. 12 (2024): e8251. https://doi.org/10.55905/oelv22n12-149.

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The increasing complexity of industrial processes demands innovative solutions for the integration and management of multi-agent systems. This work proposes a hierarchical multi-agent system architecture to unify these systems, aiming for efficiency and autonomy in complex and connected production environments. The proposal focuses on the integration of multiple tasks and layers, promoting operational freedom without compromising security and agility. To optimize the use and understanding of data flows, the system uses Object-Centric Process Mining techniques at runtime, an innovative approach

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

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Network security is one of the weakest and most sensitive areas within an organization, because it requires different strategies to protect vulnerable points as well as the coordination and distribution of efforts in order to encompass all the possible forms and points of computer attacks. In this article a distributed multiagent system is proposed as a tool to protect security in networks with different operating systems and which consequently are susceptible to diverse attacks. The system proposed is composed of heterogeneous autonomous agents with different characteristics in terms of their

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Dror, Moshe, Bruce Hartman, Gary Knotts, and Daniel Zeng. "Randomized distributed access to mutually exclusive resources." Journal of Applied Mathematics and Decision Sciences 2005, no. 1 (2005): 1–18. http://dx.doi.org/10.1155/jamds.2005.1.

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Many systems consist of a set of agents which must acquire exclusive access to resources from a shared pool. Coordination of agents in such systems is often implemented in the form of a centralized mechanism. The intervention of this type of mechanism, however, typically introduces significant computational overhead and reduces the amount of concurrent activity. Alternatives to centralized mechanisms exist, but they generally suffer from the need for extensive interagent communication. In this paper, we develop a randomized approach to make multiagent resource-allocation decisions with the obj

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Raju, Leo, R. S. Milton, and Senthilkumaran Mahadevan. "Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX." Scientific World Journal 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9858101.

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The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB t

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Johnson, W. Lewis, and James C. Lester. "Pedagogical Agents: Back to the Future." AI Magazine 39, no. 2 (2018): 33–44. http://dx.doi.org/10.1609/aimag.v39i2.2793.

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Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved lear

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Olayinka Akinbolajo, Olayinka Akinbolajo. "Enhancing Job Scheduling Efficiency through Multi-Agent Systems in Distributed Computing Environments." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 706–11. https://doi.org/10.35629/5252-0703706711.

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The rapid growth of distributed computing environments has necessitated the development of efficient job scheduling mechanisms to optimize resource utilization and minimize latency. MultiAgent Systems (MAS) have emerged as a promising approach to address the complexities of job scheduling in such environments. This paper explores the integration of MAS into distributed computing systems to enhance job scheduling efficiency. We propose a novel framework that leverages the autonomous, collaborative, and adaptive capabilities of agents to improve scheduling decisions. Through extensive simulation

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Shabani, Faridoon, Bijan Ranjbar, and Ali Ghadamyari. "An Adaptive -Based Formation Control for Multirobot Systems." ISRN Robotics 2013 (November 27, 2013): 1–12. http://dx.doi.org/10.5402/2013/192487.

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We describe a decentralized formation problem for multiple robots, where an formation controller is proposed. The network of dynamic agents with external disturbances and uncertainties are discussed in formation problems. We first describe how to design social potential fields to obtain a formation with the shape of a polygon. Then, we provide a formal proof of the asymptotic stability of the system, based on the definition of a proper Lyapunov function and technique. The advantages of the proposed controller can be listed as robustness to input nonlinearity, external disturbances, and model u

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GERO, JOHN S., and FRANCES M. T. BRAZIER. "Special Issue: Intelligent agents in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (2004): 113. http://dx.doi.org/10.1017/s0890060404040089.

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This Special Issue had its genesis in an international Workshop on Agents in Design held in June 2002, at MIT by the Guest Editors. Computational agents have been developed within the artificial intelligence community over an extended period. The concept of an agent can be traced to Carl Hewitt's 1977 work on “actors.” Hewitt defined actors as self-contained, interactive, and concurrently executing objects. Since then, considerable research has gone into developing the concept of an agent and into formalizing agents, developing multiagent systems, and exploring their use. The use of agents in

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Gascueña, José M., and Antonio Fernández-Caballero. "On the use of agent technology in intelligent, multisensory and distributed surveillance." Knowledge Engineering Review 26, no. 2 (2011): 191–208. http://dx.doi.org/10.1017/s0269888911000026.

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AbstractThis article revises the state of the art of the application of agent technology within the scope of surveillance systems. Thus, the potential of the practical use of the concepts and technologies of the agent paradigm can be identified and evaluated in this domain. Current surveillance systems are noted for using several devices, heterogeneous in many instances, distributed along the observed scenario, while incorporating a certain degree of intelligence to alert the operator proactively to what is going on in the observed scenario and prevent the operator from having to observe the m

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Weng, Yu, Haozhen Chu, and Zhaoyi Shi. "An Intelligent Offloading System Based on Multiagent Reinforcement Learning." Security and Communication Networks 2021 (March 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/8830879.

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Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent

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Si, Huaiwei, Guozhen Tan, and Hao Zuo. "A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy." Wireless Communications and Mobile Computing 2022 (June 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/9665421.

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With the growing up of Internet of Things technology, the application of Internet of Things has been popularized in the field of intelligent vehicles. Therefore, more artificial intelligence algorithms, especially DRL methods, are more widely used in autonomous driving. A large number of deep reinforcement learning (RL) technologies are continuously applied to the behavior planning module of single-vehicle autonomous driving in early. However, autonomous driving is an environment where multi-intelligent vehicles coexist, interact with each other, and dynamically change. In this environment, mu

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Patachi, Andreea-Iulia, and Florin Leon. "Multiagent Multimodal Trajectory Prediction in Urban Traffic Scenarios Using a Neural Network-Based Solution." Mathematics 11, no. 8 (2023): 1923. http://dx.doi.org/10.3390/math11081923.

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Trajectory prediction in urban scenarios is critical for high-level automated driving systems. However, this task is associated with many challenges. On the one hand, a scene typically includes different traffic participants, such as vehicles, buses, pedestrians, and cyclists, which may behave differently. On the other hand, an agent may have multiple plausible future trajectories based on complex interactions with the other agents. To address these challenges, we propose a multiagent, multimodal trajectory prediction method based on neural networks, which encodes past motion information, grou

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Balding, Steven, Amadou Gning, Yongqiang Cheng, and Jamshed Iqbal. "Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics." Applied Sciences 13, no. 8 (2023): 5065. http://dx.doi.org/10.3390/app13085065.

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Robotic agents are now ubiquitous in both home and work environments; moreover, the degree of task complexity they can undertake is also increasing exponentially. Now that advanced robotic agents are commonplace, the question for utilisation becomes how to enable collaboration of these agents, and indeed, many have considered this over the last decade. If we can leverage the heterogeneous capabilities of multiple agents, not only can we achieve more complex tasks, but we can better position the agents in more chaotic environments and compensate for lacking systems in less sophisticated agents.

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Maithripala, D. H. A., Suhada Jayasuriya, and Mark J. Mears. "Phantom Track Generation Through Cooperative Control of Multiple ECAVs Based on Feasibility Analysis." Journal of Dynamic Systems, Measurement, and Control 129, no. 5 (2007): 708–15. http://dx.doi.org/10.1115/1.2764512.

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Radar deception through phantom track generation using multiple electronic combat air vehicles is addressed, which serves as a motivating example for cooperative control of autonomous multiagent systems. A general framework to derive sufficient conditions for the existence of feasible solutions for an affine nonlinear control system comprising of a team of nonholonomic mobile agents having to satisfy actuator and interagent constraints is presented. Based on this feasibility analysis, an algorithm capable of generating trajectories online and in real time, for the phantom track generation prob

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Kardas, Geylani. "Model-driven development of multiagent systems: a survey and evaluation." Knowledge Engineering Review 28, no. 4 (2013): 479–503. http://dx.doi.org/10.1017/s0269888913000088.

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AbstractTo work in a higher abstraction level is of critical importance for the development of multiagent systems (MAS) since it is almost impossible to observe code-level details of such systems due to their internal complexity, distributedness and openness. As one of the promising software development approaches, model-driven development (MDD) aims to change the focus of software development from code to models. This paradigm shift, introduced by the MDD, may also provide the desired abstraction level during the development of MASs. For this reason, MDD of autonomous agents and MASs has been

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Mutambik, Ibrahim. "IoT-Enabled Adaptive Traffic Management: A Multiagent Framework for Urban Mobility Optimisation." Sensors 25, no. 13 (2025): 4126. https://doi.org/10.3390/s25134126.

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This study evaluates the potential of IoT-enabled adaptive traffic management systems for mitigating urban congestion, enhancing mobility, and reducing environmental impacts in densely populated cities. Using London as a case study, the research develops a multiagent simulation framework to assess the effectiveness of advanced traffic management strategies—including adaptive signal control and dynamic rerouting—under varied traffic scenarios. Unlike conventional models that rely on static or reactive approaches, this framework integrates real-time data from IoT-enabled sensors with predictive

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Elkhider, Siddig M., Omar Al-Buraiki, and Sami El-Ferik. "Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay." Applied Sciences 11, no. 19 (2021): 9145. http://dx.doi.org/10.3390/app11199145.

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This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obs

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Гунько, І. О., С. О. Кудря, П. Д. Лежнюк, and Є. О. Нікіторович. "SELF-HEALING OF ELECTRICITY SUPPLY IN INTELLIGENT LOCAL ELECTRIC POWER SYSTEM BASED ON RENEWABLE SOURCES OF ENERGY." Vidnovluvana energetika, no. 1(80) (March 31, 2025): 6–12. https://doi.org/10.36296/1819-8058.2025.1(80).6-12.

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With the increase in the power grid capacities of renewable sources of energy (RSE) and the implementation of measures and means to compensate for the dependence of their generation on natural conditions, the role and importance of RSE in electric power systems (EPS) is changing. RSE are real opportunity to decentralize electricity generation and provide power supply systems with a reliable source of energy. It is shown that it is advisable to do this in the form of local electric power systems (LEPS), which operate in normal modes in parallel with the EPS as balancing groups, and in extreme c

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García-Vázquez, Juan, Marcela Rodríguez, Monica Tentori, Diana Saldaña, Ángel Andrade, and Adán Espinoza. "An Agent-based Architecture for Developing Activity-Aware Systems for Assisting Elderly." JUCS - Journal of Universal Computer Science 16, no. (12) (2010): 1500–1520. https://doi.org/10.3217/jucs-016-12-1500.

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Ageing is a global phenomenon which has motivated many research and development projects with the aim of providing computing services that support the active and independent living of the elderly. To integrate the ambient intelligence (AmI) vision into the home environment to allow elders to "age in place", it has been identified the necessity of providing high-level software support for creating ambient assisted living (AAL) environments. We propose activity-aware computing to allow smart environments to provide continuous activity awareness and opportunistically offer assistance aimed at sup

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Gharbi, Atef, Mohamed Ayari, Nasser Albalawi, Yamen El Touati, and Zeineb Klai. "A Comparative Analysis of Fairness and Satisfaction in Multi-Agent Resource Allocation: Integrating Borda Count and K-Means Approaches with Distributive Justice Principles." Mathematics 13, no. 15 (2025): 2355. https://doi.org/10.3390/math13152355.

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This study introduces a novel framework for fair resource allocation in self-governing, multi-agent systems, leveraging principles of interactional justice to enable agents to autonomously evaluate fairness in both individual and collective resource distribution. Central to our approach is the integration of Rescher’s canons of distributive justice, which provide a comprehensive, multidimensional framework encompassing equality, need, effort and productivity to assess legitimate claims on resources. In resource-constrained environments, multiagent systems require a balance between fairness and

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Vasirani, M., and S. Ossowski. "A Market-Inspired Approach for Intersection Management in Urban Road Traffic Networks." Journal of Artificial Intelligence Research 43 (April 24, 2012): 621–59. http://dx.doi.org/10.1613/jair.3560.

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Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to b

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Hanna, Josiah P., Siddharth Desai, Haresh Karnan, Garrett Warnell, and Peter Stone. "Grounded action transformation for sim-to-real reinforcement learning." Machine Learning 110, no. 9 (2021): 2469–99. http://dx.doi.org/10.1007/s10994-021-05982-z.

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AbstractReinforcement learning in simulation is a promising alternative to the prohibitive sample cost of reinforcement learning in the physical world. Unfortunately, policies learned in simulation often perform worse than hand-coded policies when applied on the target, physical system. Grounded simulation learning (gsl) is a general framework that promises to address this issue by altering the simulator to better match the real world (Farchy et al. 2013 in Proceedings of the 12th international conference on autonomous agents and multiagent systems (AAMAS)). This article introduces a new algor

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Alqurashi, Raghda, and Tom Altman. "Hierarchical Agent-Based Modeling for Improved Traffic Routing." Applied Sciences 9, no. 20 (2019): 4376. http://dx.doi.org/10.3390/app9204376.

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Agent-based model (ABM) simulation is a bottom–up approach that can describe the phenomena generated from actions and interactions within a multiagent system. An ABM is an improvement over model simulations which only describe the global behavior of a system. Therefore, it is an appropriate technology to analyze emergent phenomena in social sciences and complex adaptive systems such as vehicular traffic and pedestrian crowds. In this paper, a hybrid agent-based modeling framework designed to automate decision-making processes during traffic congestion is proposed. The model provides drivers wi

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Kovalev, S. P., and P. V. Sorokoletov. "Government Control and Regulation in Health Care in Digital Economy Epoch." Administrative Consulting, no. 4 (June 7, 2018): 53–62. https://doi.org/10.22394/1726-1139-2018-4-53-62.

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Approaching to health care digital economy gives dramatic new opportunities for regulation. At the same time information transparency leads to civil society growth of trust. The Russian “Digital Economy” program provides considerable means until 2024. The Ministry of Health also develops the Uniform state information system of health care. Compliance of these efforts to modern scientific paradigms of digital economy and to the nowadays world trends in digital health care with the purpose to estimate correctness of the directions and approaches chosen a

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Guessoum, Z. "Adaptive agents and multiagent systems." IEEE Distributed Systems Online 5, no. 7 (2004): 1–4. http://dx.doi.org/10.1109/mdso.2004.10.

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Purushotham, Dr M. "Advanced Key Foundations of Multiagent System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1–6. http://dx.doi.org/10.22214/ijraset.2023.49153.

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Abstract: Ethics is inherently a multiagent concern. However, analysis on AI ethics nowadays is dominated by work on individual agents: (1) however Associate in Nursing autonomous golem or automotive could hurt or (differentially) profit folks in theoretical things (the questionable tramcar problems) and (2) how a machine learning formula could turn out biased choices or recommendations. The social group framework is basically omitted. To develop new foundations for ethics in AI, we tend to adopt a sociotechnical stance during which agents (as technical entities) facilitate autonomous social e

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WEYNS, DANNY, MICHAEL SCHUMACHER, ALESSANDRO RICCI, MIRKO VIROLI, and TOM HOLVOET. "Environments in multiagent systems." Knowledge Engineering Review 20, no. 2 (2005): 127–41. http://dx.doi.org/10.1017/s0269888905000457.

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There is a growing awareness in the multiagent systems research community that the environment plays a prominent role in multiagent systems. Originating from research on behavior-based agent systems and situated multiagent systems, the importance of the environment is now gradually being accepted in the multiagent system community in general. In this paper, we put forward the environment as a first-order abstraction in multiagent systems. This position is motivated by the fact that several aspects of multiagent systems that conceptually do not belong to agents themselves should not be assigned

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Ahmed, Moamin, Mohd Sharifuddin Ahmad, and Mohd Zaliman M. Yusoff. "A Collaborative Framework for Multiagent Systems." International Journal of Agent Technologies and Systems 3, no. 4 (2011): 1–18. http://dx.doi.org/10.4018/jats.2011100101.

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In this paper, the authors demonstrate the use of software agents to extend the role of humans in a collaborative work process. The extended roles to agents provide a convenient means for humans to delegate mundane tasks to software agents. The framework employs the FIPA ACL communication protocol which implements communication between agents. An interface for each agent implements the communication between humans and agents. Such interface and the subsequent communication performed by agents and between agents contribute to the achievement of shared goals.

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