Two-time-scale multi-agent systems under rotation-scale attacks: Asynchronous dynamic event-triggered consensus

RaSA-BoDX: A meta-cognitive reasoning framework for cyberbullying language detection and mitigation using multi-agent systems

Water distribution networks (WDNs), a critical part of urban infrastructure, normally require numerous model simulations for effective planning and management. However, traditional WDN modelling requires complex workflows and specialized expertise. EPANET is the most widely adopted modelling tool for WDN hydraulics and water quality simulations, yet its operational complexity restricts accessibility and slows timely decision-making. Recent advances in large language models (LLMs) have led to the development of agentic artificial intelligence systems that autonomously coordinate tasks and control complex engineering simulations through natural language prompts. Here we introduce EPANET-Agentic, a multi-agent system that integrates advanced workflow reasoning with the EPANET simulator and incorporates human-in-the-loop oversight for critical interventions. The new platform adopts an orchestrator-centred, tool-driven architecture that nests three specialised agents (TaskExecutor, CodeRunner, and DataAnalyzer) as function-call tools. This design enables autonomous task decomposition, precise tool invocation, and transparent workflow management. The abilities of EPANET-Agentic are evaluated on three benchmark networks (i.e., L-Town, C-Town, and Net3) across four categories of tasks: System Characteristics, System Dynamics, System Operation, and Scenario Simulation. The results demonstrate that EPANET-Agentic achieved a 100% success rate and tool invocation accuracy with no human interventions. Moreover, the multimodal DataAnalyzer agent provided valid interpretations of simulation results, while the nested tool design ensured robustness and the architecture exhibited strong scalability across diverse hydraulic analysis tasks. These findings confirm that EPANET-Agentic enables natural language-controlled WDN simulation and analysis with engineering-grade reliability, while still adhering to a human-in-the-loop approach required for safety-critical systems. With its modular architecture and strong adaptability, EPANET-Agentic marks a step change from conventional WDN modelling approaches, positioning itself as a next-generation platform for complex planning and management challenges.

Heterogeneous multi-agent fleet control system for material handling in a Software-Defined Factory

Adaptive multi-agent stock trading decision support system based on deep reinforcement learning

Distributed fixed-time tracking control of low-order nonlinear multi-agent systems

The role of arc rotation on improving consensus convergence rate for a leader-follower multi-agent system

Privacy preservation-based dynamic event-triggered bipartite consensus strategy for nonlinear multi-agent systems with unknown mismatched disturbances

Consensus of multi-agent systems under variable denial-of-service attacks: Noise-based event-triggered protocols

This work proposes a multi-agent system aimed at increasing the computing sustainability of high-performance computing data centers that are distributed among several wind farms. The novel approach of wind turbines housing high-performance computing data centers seeks to maximize renewable energy usage by supplying the data centers with otherwise curtailed wind energy, thus increasing wind farm efficiency as well. To optimize data center operation in this unique environment, job execution should be prioritized during periods of high availability of renewable energy. When wind power generation is low, resource utilization should be continuously adjusted to minimize gray electricity consumption with high carbon intensity or high grid consumption costs. Furthermore, green service-level agreements are introduced allowing for more flexibility in terms of deadline compliance, thereby fostering energy-aware data center operation. The proposed multi-agent system realizes a moving-horizon, multi-objective optimization problem to find the best operational strategy, taking into account both sustainability and performance concerns, and is compared against a selection of baseline job scheduling strategies. • Historic Data-Driven Data Center Placement Strategy. • Scalable Multi-Agent System for Energy-Optimized Data Center Operation. • Two-step Moving-Horizon (Multi-Objective) Optimization Approach for Scheduling. • Implementation of Green Service-Level Agreements. • Thorough Evaluation of the Optimization Approach.

Fixed-time air-ground cooperative time-varying formation-containment control of heterogeneous multi-agent systems with well-informed followers

Finite-horizon optimal herdability control for hierarchical linear multi-agent systems with signed weighted graphs

Fixed-time adaptive fuzzy event-triggered fault-tolerant containment control for nonlinear multi-agent systems

Fault-tolerant control of multi-agent systems using a Fourier series observer under DoS attacks and periodic intermittent faults

Multi-agent systems based on hierarchical voltage control in active low- and medium-voltage electrical distribution networks

Time-varying formation control of multi-agent systems with dynamic event triggering strategies under denial-of-service attacks

DABLSE-based adaptive finite-time bipartite consensus for multi-agent systems with noncooperative leader

From insight to action: Embodied multi-agent system integrating vision language model for digital twin-assisted human-robot collaborative assembly

ABSTRACT This paper investigates the time‐varying group formation‐containment (TVGFC) tracking problem for multi‐agent systems (MASs) with input saturation, and simultaneously accounts for unknown control inputs. The system consists of three types of agents divided into different subgroups: Formation leaders, followers, and tracking leaders. These agents are respectively responsible for providing macroscopic motion references, achieving sub‐formation tracking, and converging to the convex hull within their corresponding subgroups. Firstly, by adjusting the structure of the Laplacian matrix that characterizes inter‐agent interactions, the subdivision of all agents into subgroups is achieved. Then, an observer‐based TVGFC tracking control protocol and a feasible constraint for formation tracking are proposed. Theoretical analysis confirms that the proposed protocol ensures the achievement of the desired TVGFC tracking. Furthermore, a continuous control protocol is designed to suppress chattering resulting from the unknown input. The proposed protocol guarantees that the errors remain ultimately bounded, which can be asymptotically driven into an arbitrarily small neighborhood of zero. Ultimately, a numerical simulation validates the effectiveness of the proposed control scheme.

Abstract We present a formal framework encompassing concurrency theoretic and modal logic based approaches to the modeling and verification of dynamic multi-agent systems. We develop a model of computation merging classical labeled transition systems and multi-agent Kripke frames. Based on this model, which we call a Kripke labeled transition system, we provide a modal logic and a process algebra for the specification and analysis of interacting multi-agent systems. Our logic is obtained by combining proof systems for Hennessy-Milner Logic and the normal epistemic logic $$\textsf{S5}_n$$ S 5 n and is sound and complete with respect to its intended models. Further, we show that this logic is decidable and induces a behavioral equivalence combining classical notions of bisimulation. We prove that our process algebra is adequate for specifying the behavior of Kripke labeled transition systems and we show its effectiveness and usability through real-world examples.