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Scheduling in decentralized manufacturing systems is being expanded and transformed to a smart level where manufacturing tasks are executed autonomously. However, achieving self-organized manufacturing resources that can make collaborative ...

This paper considers consensus strategy design for double-integrator multi-agent systems with matched disturbances under a directed graph. Specifically, we consider the case where both the control inputs and velocities of the agents are subject ...

The stabilizing switching signal design of discrete-time linear compartmental switched systems (DT-LCSSs) has been heretofore unsolved. It has been proven that a DT-LCSS is stabilizable if and only if it is stabilizable by a periodic switching ...

In this paper, a smoothing approximation-based adaptive neurodynamic approach is proposed for a nonsmooth resource allocation problem (NRAP) with multiple constraints. The smoothing approximation method is combined with multi-agent systems to ...

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton–Jacobi–Isaacs equation is derived by the ...

Centralized Training with Decentralized Execution (CTDE) is a prevalent paradigm in the field of fully cooperative Multi-Agent Reinforcement Learning (MARL). Existing algorithms often encounter two major problems: independent strategies tend to ...

• We propose an optimistic Q-value to explore the potential value of individual policies.
• We introduce specially designed motivational communication modules to enhance teamwork.
• We show that OSSMC can achieve a competitive baseline ...

Flocking cohesion is a crucial factor for groups to maintain aggregation. Communication barriers can significantly challenge the maintenance of flocking aggregation and integrity. Although several studies have focused on communication barriers ...

How do we know when a reinforcement learning policy needs to adapt? In non-stationary environments, agents must adapt and learn in environments that change dynamically. We propose a finite-horizon model-free solution using a hierarchical learning ...

As the power industry becomes increasingly complex, Digital Twin (DT) technology has emerged as a crucial tool for enhancing grid resilience and operational efficiency by creating dynamic digital replicas of physical systems. These replicas enable ...

An fault-tolerant tracking control of uncertain nonlinear multi-agent systems with state constraints and input saturation is investigated. A saturated controller is processed by converting the saturation function into a linear form of the control ...

A significant part of CO₂ emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful ...

• We propose a recommendation algorithm for generating device usage recommendations.
• Multi-agent architecture supports seamless functionality expansion.
• We suggest the performance evaluation framework for our recommendation system.

Agent negotiation in multi-agent systems has been extensively studied, focusing on both theoretical and applied research. However, a limited number of studies have considered proposing an offer-generating strategy for agents to propose offers ...

• Fill the research gap of multi-negotiations with mixed-types of issues.
• Propose a unified modeling for multi-issue negotiations scenario.
• Propose an algorithm named HPGA as the offer-generating strategy.
• Help agents to achieve ...

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-...

Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible ...

• Turbulent trade environments have highlighted the need for organisations to enhance the resilience and diversity of their supply chains.
• The model of autonomous supply chains, characterised by predictive and self-decision-making ...

In the realm of fully cooperative multi-agent reinforcement learning (MARL), effective communication can induce implicit cooperation among agents and improve overall performance. In current communication strategies, agents are allowed to exchange ...

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• A novel MARL communication protocol named HyperComm is proposed.
• The hypergraph is utilized in multi-agent communication for the first time.
• A two-stage information broadcasting approach is designed.
• The performance of ...

It is well known that nonlinear multi-agent systems (MASs) with a directed topology can reach a consensus if the global coupling coefficient is strong enough. In this paper, we propose a novel distributed protocol based on the subgroup structure. ...

Digital Twins (DTs) are used to augment physical entities by exploiting assorted computational approaches applied to the virtual twin counterpart. A DT is generally described as a physical entity, its virtual counterpart, and the data ...

The Variable Aquila Optimization Algorithm, designed especially for Multi-Robot Search in uncharted territory, is introduced in this paper. The framework, called Coordinated Multi-robot Exploration Variable Aquila Optimizer (CME-VAO), combines a ...

This paper considers the exponential consensus of nonlinear switched multi-agent systems (MASs) subject to random attacks. The proposed model is enhanced by introducing the concept of mode-dependent average dwell time (MDADT) and the one-sided ...

In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been made in the research of algorithms that combine Reinforcement Learning (RL) with Attention Mechanism (AM). However, there is a lack of comprehensive reviews ...