Multi-agent System Model Research Articles

Distributed Consensus Tracking of Incommensurate Heterogeneous Fractional-Order Multi-Agent Systems Based on Vector Lyapunov Function Method

This paper investigates the tracking problem of fractional-order multi-agent systems. Both the order and parameters of the leader are unknown. Firstly, based on the positive system approach, the asymptotically stable criteria for incommensurate linear fractional-order systems are derived. Secondly, the models of incommensurate heterogeneous multi-agent systems are introduced. To cope with incommensurate and heterogeneous situations among followers and the leader, radial basis function neural networks (RBFNNs) and a discontinuous control method are used. Thirdly, the consensus criteria are derived by using the Vector Lyapunov Function method. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical method.

Personalized Education via Mobile Technology Flexible Learning Frameworks

The prevalence of mobile and ubiquitous computing equipment is transforming how learners conduct their education. Nevertheless, the bulk of educational content generated for desktop platforms is unacceptable for handheld gadgets. Furthermore, some materials that are irrelevant to the preferences of the learner or the contextual environment might reduce the effect of learning and elevate the cost of communication. Furthermore, there is a deficit in the academic structure for mobile learning. This paper proposes personalized adaptation techniques for customized modification contents. This in turn strives to offer adaptive content contingent on learner experience and device capabilities. On top of that, it recommends a few alternatives to producing intelligent and adaptive material for children. Numerous facets of the study are examined to employ the benefits of learner, device, and location modeling in tandem with mobile technology to provide a personalized spread of multimedia-rich resources at any time or anywhere. As an outcome, it delivers proper learning resources and applies the learning style most suitable to the demands and tastes of each learner (personalized learning). Our system enables several types of learning styles to pick from, but the most significant aspect is to pair each learner’s characteristics with the right program and learning style. A novel chance for a broad range of learners to boost their knowledge in a tailored manner is made accessible by the adaptation of the material and styles, which enables the learning process to be dynamic. Additionally, our system’s resilience and interaction have risen owing to multi-agent system modeling.

Modeling biological memory network by an autonomous and adaptive multi-agent system

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a multi-agent system (MAS) model based on the graph theory, each agent equipped with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information memory, modeling and simulation of the brain’s memory process. This decentralized approach transforms memory storage into the management of MAS paths, with each agent utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model’s unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Emulating neuron electrophysiology, each agent’s adaptive learning behavior is represented through a microcircuit centered around a variable resistor. Using principles of Ohm’s and Kirchhoff’s laws, we validated the model’s efficacy in memorizing and retrieving data through computer simulations. This approach offers a plausible neurobiological explanation for memory realization and validates the memory trace theory at a system level.

A Survey of Multi-Agent Systems for Smartgrids

This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems.

Optimization of spatial layouts for underground facilities to achieve carbon neutrality in cities: A multi-agent system model

Subways, underground logistics systems and underground parking, as the primary facilities types of underground, contribute significantly to the achievement of carbon–neutral cities by moving surface transportation to underground, thereby releasing surface space for the creation of more urban blue-green space for carbon sink. Therefore, in-depth studies on carbon neutrality strategies as well as reliable layout optimization solutions of these three types of underground facilities are required. This study proposes a spatial layout optimization strategy for carbon neutrality using underground hydrogen storage and geothermal energy for these three types of underground facilities employing a multi-agent system model. First, three spatial layout relationships, competition, coordination, and followership, between five underground facilities that contribute to emission reduction were investigated. Second, the implementation steps for optimizing the spatial layout of underground facilities were determined by defining the behavioral guidelines for spatial environment, underground facility, and synergistic agent. Finally, using the Tianfu New District in Chengdu City, China, as a case study, layouts of underground facilities under three different underground space development scenarios were simulated to verify the model. The findings of this study address the gap in the research on underground spatial facilities and their layout optimization in response to emission reduction. This study provided a significant reference for the study of underground space and underground resources at the planning level to aid in achieving carbon–neutral cities.

Stability Analysis for Large-Scale Multi-Agent Molecular Communication Systems.

Molecular communication (MC) is recently featured as a novel communication tool to connect individual biological nanorobots. It is expected that a large number of nanorobots can form large multi-agent MC systems through MC to accomplish complex and large-scale tasks that cannot be achieved by a single nanorobot. However, most previous models for MC systems assume a unidirectional diffusion communication channel and cannot capture the feedback between each nanorobot, which is important for multi-agent MC systems. In this paper, we introduce a system theoretic model for large-scale multi-agent MC systems using transfer functions, and then propose a method to analyze the stability for multi-agent MC systems. The proposed method decomposes the multi-agent MC system into multiple single-input and single-output (SISO) systems, which facilitates the application of simple analysis technique for SISO systems to the large-scale multi-agent MC system. Finally, we demonstrate the proposed method by analyzing the stability of a specific large-scale multi-agent MC system and clarify a parameter region to synchronize the states of nanorobots, which is important to make cooperative behaviors at a population level.

Decentralized leader-following consensus control design for discrete-time multi-agent systems with switching topology

The problem of consensus control of linear discrete-time multi-agent systems (MASs) with switching topology is considered in the presence of a leader. The goal of consensus control is to bring the states of all agents to the leader state while providing stability for local agents, as well as the MAS as a whole. In contrast to the traditional approach, which uses the concept of an extended dynamic multi-agent system model and communication topology graph Laplacian, this paper proposes a decomposition approach, which provides a separate design of local controllers. The control law is chosen in the form of distributed feedback with discrete PID controllers. The problem of local controllers’ design is reduced to a set of semidefinite programming problems using the method of invariant ellipsoids. Sufficient conditions for agents’ stabilization and global consensus condition fulfillment are obtained using the linear matrix inequality technique. The availability of information about a finite set of possible configurations between agents allows us to design local controllers offline at the design stage. A numerical example demonstrates the effectiveness of the proposed approach.

Model-free adaptive bipartite consensus control for unknown heterogeneous nonlinear MASs with different input delays

In this paper, a novel model-free adaptive bipartite consensus control scheme is proposed for unknown heterogeneous nonlinear multi-agent systems (MASs) with different input delays. Firstly, an equivalent data model is established for each agent by using the dynamic linearisation method. Secondly, based on an established equivalent data model and constructed predictive compensation algorithm, the distributed model-free adaptive bipartite consensus control scheme (DMFABCC) is designed by only utilising the measured input/output data rather than the state-space model of MASs. Next, by selecting an appropriate Lyapunov function, the convergence of bipartite tracking error is rigorously proved. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed DMFABCC scheme for achieving the bipartite consensus target.

Differentially private consensus and distributed optimization in multi-agent systems: A review

In the past few decades, distributed multi-agent system (MAS) control has received growing attention due to its numerous advantages. Nonetheless, the substantial reliance on local information exchange in distributed MAS control has given rise to significant privacy concerns. Differential privacy (DP), a mathematically rigorous privacy notion, has gained popularity as a means of safeguarding privacy across multiple fields, including distributed MAS control. In this paper, we present an in-depth overview of the techniques for preserving DP in distributed MAS control, concentrating on consensus and distributed optimization. We begin by outlining the defining features and modeling of MASs from the control theory perspective. Then, we illustrate the motivation for adopting differentially private mechanisms to protect the privacy of distributed MAS control and present the fundamental principles of DP. Based on them, we investigate the cutting-edge techniques designed to preserve DP in consensus and distributed optimization. This review sheds light on the current landscape of DP applications in distributed MAS control and lays the groundwork for future progress in this essential field.

Interdependent water and power infrastructure model (IWPIM): A modeling approach for water and energy resource management in rural communities

The importance of the dependencies between water and power systems is more acutely perceived when challenges emerge. As both energy and water supply are limited, efficient use is a must for any sustainable future, especially in rural areas. Although important, a modeling tool that can analyze water-energy systems interdependencies in rural systems, at the architectural level highlighting the physical interconnections and synergies of these systems, is still lacking. We present a multi-agent system model that captures the features of both systems, at the same levels of fidelity and resolution, with coordinated operations and contingency components represented. Unlike other models, ours captures architectural features of both systems and technical constraints of the systems’ components, which is critical to capture physical intricacies of the interplay between systems components and shed light on the impacts of disruptions of either system on the other. This model, which includes multiple infrastructure components, shows the importance of a holistic understanding of the systems, for cooperation across systems physical boundaries and enhanced benefits at larger scales. This study looks to investigate water-power resource management in an irrigation system via the analysis of physical links and highlight strengths and vulnerabilities. The effects of water shortage, water re-allocation and load shedding are analyzed through scenarios designed to illustrate the utility of such a model. Results highlights the importance of inter-reservoir relationships for alleviating effects of disruption and unforeseen rise in energy demand. Water storage is also critical, helping to mitigate the impacts of water scarcity, and by extension, to keep the energy system unaffected. It can be a viable part of the solution to compensate for the negative impact of shortage for both resources.

Automated Mapping Tool from Moise+ to Colored Petri Nets

The demand for systems incorporating artificial intelligence, such as multi-agent systems, is continually increasing. Simultaneously, there is a growing need for developing tools to support this field, ensuring better fault tolerance within projects. This is particularly crucial given that these systems possess characteristics that render them non-deterministic, thereby amplifying the challenge of conducting tests. To address this challenge, a mapping tool has been developed. This tool automatically generates a graphical model, facilitating the identification of test paths for a given multi-agent system. It operates by taking an XML file from Moise+, an organizational model for multi-agent systems, and translates it into a colored Petri net. The resulting mapping serves as a foundation for generating test cases essential for validating the Moise+ model, guiding system testing. Automation streamlines the process, enhancing speed, and eliminating the potential for human error.

Impulsive Control of Variable Fractional-Order Multi-Agent Systems

The main goal of the paper is to present and study models of multi-agent systems for which the dynamics of the agents are described by a Caputo fractional derivative of variable order and a kernel that depends on an increasing function. Also, the order of the fractional derivative changes at update times. We study a case for which the exchanged information between agents occurs only at initially given update times. Two types of linear variable-order Caputo fractional models are studied. We consider both multi-agent systems without a leader and multi-agent systems with a leader. In the case of multi-agent systems without a leader, two types of models are studied. The main difference between the models is the fractional derivative describing the dynamics of agents. In the first one, a Caputo fractional derivative with respect to another function and with a continuous variable order is applied. In the second one, the applied fractional derivative changes its constant order at each update time. Mittag–Leffler stability via impulsive control is defined, and sufficient conditions are obtained. In the case of the presence of a leader in the multi-agent system, the dynamic of the agents is described by a Caputo fractional derivative with respect to an increasing function and with a constant order that changes at each update time. The leader-following consensus via impulsive control is defined, and sufficient conditions are derived. The theoretical results are illustrated with examples. We show with an example the leader’s influence on the consensus.

Natural Strategic Ability in Stochastic Multi-Agent Systems

Strategies synthesized using formal methods can be complex and often require infinite memory, which does not correspond to the expected behavior when trying to model Multi-Agent Systems (MAS). To capture such behaviors, natural strategies are a recently proposed framework striking a balance between the ability of agents to strategize with memory and the complexity of the model-checking problem, but until now has been restricted to fully deterministic settings. For the first time, we consider the probabilistic temporal logics PATL and PATL∗ under natural strategies (NatPATL and NatPATL∗). As main result we show that, in stochastic MAS, NatPATL model-checking is NP-complete when the active coalition is restricted to deterministic strategies. We also give a 2NEXPTIME complexity result for NatPATL∗ with the same restriction. In the unrestricted case, we give an EXPSPACE complexity for NatPATL and 3EXPSPACE complexity for NatPATL*.

Proactive cooperative consensus control for a class of human-in-the-loop multi-agent systems with human time-delays

In this study, we consider a class of human-in-the-loop (HiTL) multi-agent systems that divide agents into two parts: the nonautonomous agents controlled by human operators, and the autonomous agents. First, the human operators’ models are incorporated into the multi-agent system for constructing an HiTL multi-agent system model. Next, the cooperative consensus control problem is explored for the HiTL multi-agent system, and a method is proposed that enables autonomous agents to proactively collaborate with human-controlled agents in order to mitigate the effects of human time-delays. We first obtain the consensus sufficient condition for the HiTL multi-agent system without human time-delays, and then give the method by only designing the autonomous agents’ control gains to guarantee consensus. Subsequently, the delay-dependent consensus sufficient condition for the HiTL multi-agent system with human time-delays is also offered, and the method of controller design is updated for this case. Finally, a simulation experiment was designed, and the simulation results are presented to illustrate the effectiveness of the proposed methods.

Distributed Adaptive Fault-Tolerant Control for High-Speed Trains Using Multi-Agent System Model

Distributed Adaptive Fault-Tolerant Control for High-Speed Trains Using Multi-Agent System Model

HWMP-based secure communication of multi-agent systems

Multi-agent systems (MAS), known for their autonomy, fault tolerance, and flexible collaboration capabilities, are widely used in various fields. Communication security is one of the most critical problems of MAS tasks. Once a malicious attacker invades agents in the system, it brings massive threats to the entire system. It tampers with routing messages, discards packets, and paralyzes the communication network. To implement collaboration tasks of MASs safely and efficiently, this work makes security improvements on the hybrid wireless mesh protocol (HWMP) with an additional authentication system. We enhance the proactive and reactive mode of the HWMP and apply it to an integrated system, i.e., the MAS formation control as a proper instance. We present a HWMP-based secure communication model (HSCM) for MASs. The theoretical analysis and simulation results demonstrate a significant enhancement in network performance under various threats, including packet loss rate, throughput, etc. The results indicate that HSCM plays a significant role in resisting common attacks (such as black hole and flooding attacks) when compared to multi-agent systems without any security protection. There is an approximate 30% increase in packet delivery rate, a substantial enhancement in throughput, and a reduction in routing overhead. The latency also fluctuates within reasonable bounds. Our solution provides secure communication for formation control under malicious attacks.

Perspective Games

We introduce and study perspective games , which model multi-agent systems in which agents can view only the parts of the system that they own. As in standard multi-player turn-based games, the vertices of the game graph are partitioned among the players. Starting from an initial vertex, the players jointly generate a computation, with each player deciding the successor vertex whenever the generated computation reaches a vertex she owns. A perspective strategy for a player depends only on the history of visits in her vertices. Thus, unlike observation-based models of partial visibility, where uncertainty is longitudinal—players partially observe all vertices in the history, uncertainty in the perspective model is transverse—players fully observe part of the vertices in the history. We consider deterministic and probabilistic perspective games, with structural (e.g., Büchi or parity) and behavioral (e.g., LTL formulas) winning conditions. For these settings, we study the theoretical properties of the game as well as the decidability and complexity of the problem of deciding whether a player has a winning perspective strategy, in terms of both the game graph and the objectives. We compare perspective strategies with memoryless ones, and study an extension of the temporal logic ATL ⋆ with path quantifiers that capture perspective and memoryless strategies.

Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

The demand for resilient and flexible supply chains has become even more apparent in the face of disruptions such as the COVID-pandemic and the ongoing Russia-Ukraine War. Whilst multi-agent system approaches have been proposed to increase resilience in supply chains for more than two decades, their development remains limited due to their difficulty of implementation and black box nature. The emergence of modern AI approaches including foundation models, enables the creation of generalist agents with multi-faceted decision-making capabilities. This opens up opportunities to create supply chain systems with self-orchestrating capabilities and heightened resilience, in a way that is human understandable, through natural language text. However, unlike areas such as healthcare and finance, the application of modern agent technology in the supply chain domain is under explored. This paper thus aims to conceptually explore this less studied domain, investigating the convergence between foundation models, multi-agent systems, and supply chain management. We discuss the opportunities and challenges arising from this convergence for enabling autonomous supply chains, and propose key future research topics to advance this convergence.

Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

A Multi-Agent System Model to Advance Artificial General Intelligence based on Piaget’s Theory of Cognitive Development

A Multi-Agent System Model to Advance Artificial General Intelligence based on Piaget’s Theory of Cognitive Development