This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter contro...This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.展开更多
Interaction is one of the crucial features of multl-agent systems, in which there are two kinds of interaction： agent-to-agent and human-to-agent. In order to unify the two kinds of interaction while designing multi-...Interaction is one of the crucial features of multl-agent systems, in which there are two kinds of interaction： agent-to-agent and human-to-agent. In order to unify the two kinds of interaction while designing multi-agent systems, this paper introduces Q language-a scenario description language for designing interaction among agents and humans. Based on Q, we propose an integrating interaction framework system for multi-agent coordination, in which Q scenarios are used to uniformly describe both kinds of interactions. Being in accordance to the characteristics of Q language, the Q-based framework makes the interaction process open and easily understood by the users. Additionally, it makes specific applications of multi-agent systems easy to be established by application designers. By applying agent negotiation in agent-mediated e-commerce and agent cooperation in interoperable information query on the Semantic Web, we illustrate how the presented framework for multi-agent coordination is implemented in concrete applications. At the same time, these two different applications also demonstrate usability of the presented framework and verify validity of Q language.展开更多
For estimation group competition and multiagent coordination strategy, this paper introduces a notion based on multiagent group. According to the control domain, it analyzes the multiagent strategy during competition ...For estimation group competition and multiagent coordination strategy, this paper introduces a notion based on multiagent group. According to the control domain, it analyzes the multiagent strategy during competition in the macroscopic. It has been adopted in robot soccer and result enunciates that our method does not depend on competition result. It can objectively quantitatively estimate coordination strategy.展开更多
This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents inter...This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.展开更多
This paper presents an adaptive multi-agent coordination(AMAC)strategy suitable for complex scenarios,which only requires information exchange between neighbouring robots.Unlike traditional multi-agent coordination me...This paper presents an adaptive multi-agent coordination(AMAC)strategy suitable for complex scenarios,which only requires information exchange between neighbouring robots.Unlike traditional multi-agent coordination methods that are solved by neural dynamics,the proposed strategy displays greater flexibility,adaptability and scalability.Furthermore,the proposed AMAC strategy is reconstructed as a time-varying complex-valued matrix equation.By introducing a dynamic error function,a fixed-time convergent zeroing neural network(FTCZNN)model is designed for the online solution of the AMAC strategy,with its convergence time upper bound derived theoretically.Finally,the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments.Numerical results indicate that this method can reduce the formation error to the order of 10^(-6)within 1.8 s.展开更多
Under solvothermal conditions,1,4‑naphthalenedicarboxylic acid(H_(2)ndc)and 9,9′‑dihexyl‑2,7‑di(pyridin‑4‑yl)fluorene(hfdp)reacted with Co^(2+)ions and Cd^(2+)ions to form two coordination polymers,[Co(hfdp)(ndc)(H2O...Under solvothermal conditions,1,4‑naphthalenedicarboxylic acid(H_(2)ndc)and 9,9′‑dihexyl‑2,7‑di(pyridin‑4‑yl)fluorene(hfdp)reacted with Co^(2+)ions and Cd^(2+)ions to form two coordination polymers,[Co(hfdp)(ndc)(H2O)]·DMA}n(1)and{[Cd(hfdp)(ndc)(H_(2)O)]·DMA}_(n)(2),respectively(DMA=N,N‑dimethylacetamide).Single‑crystal X‑ray diffraction analyses showed that both complexes 1 and 2 contain similar structures.Topological analysis indicates that complexes 1 and 2 have a{44·62}planar structure.In addition,both complexes reveal good thermal stability and fluorescence sensing performance.They exhibited good sensitivity and selectivity towards 2,4,6‑trinitrophenol(TNP)by fluorescent quenching.The limits of detection of 1 and 2 for TNP were 0.107 and 0.327μmol·L^(-1),respectively.CCDC:2475515,1;2475516,2.展开更多
The ionothermal reaction between CuCl_(2),1,4-bis(1,2,4-triazol-1-ylmethyl)benzene(BBTZ),and(NH_(4))_(6)Mo_(7)O_(24) in 1-ethyl-3-methylimidazolium bromide((Emim)Br)led to a new octamolybdate-based coordination polyme...The ionothermal reaction between CuCl_(2),1,4-bis(1,2,4-triazol-1-ylmethyl)benzene(BBTZ),and(NH_(4))_(6)Mo_(7)O_(24) in 1-ethyl-3-methylimidazolium bromide((Emim)Br)led to a new octamolybdate-based coordination polymer(Emim)2[Cu(BBTZ)_(2)(β-Mo_(8)O_(26))](Mo_(8)-CP).Mo_(8)-CP was characterized by elemental analysis,thermogravime-try,IR,powder X-ray diffraction,and single-crystal X-ray diffraction.In Mo_(8)-CP,structural analysis reveals that Cu coordinates with BBTZ ligands to form an interlocked 1D chain.These chains are further bridged by(β-Mo_(8)O_(26))^(4-)to construct a 3D coordination polymer.Notably,(Emim)^(+)acts as a structure-directing agent,occupying the channels of the 3D coordination polymer.Based on this unique structure,the ion exchange properties of Mo_(8)-CP toward rare-earth ions were investigated.It has been found that the luminescent color of the material can be successfully regulat-ed by introducing Eu^(3+)or Tb^(3+)through ion exchange.CCDC:2475110,Mo_(8)-CP.展开更多
Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2...Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2)O(1)was synthesized as a 2D structure using Coas the metal source,methanol‑water(4∶6,V/V)as the solvent,and specific concentrations of 2,5‑furandicarboxylic acid(H_(2)FDCA)and 1,3,5‑triimidazole benzene(L).Adjusting to pure water and lowering the concentration of L yielded the 1D chain structure of[Co(HL)2(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(2).Using Cu(Ⅱ)as the metal source,methanol/water(9∶1,V/V)as the solvent,and specific concentrations of L and H2FDCA,the 1D chain structure of[Cu(L)(FDCA)(H_(2)O)]·2H_(2)O(3)was synthesized.Upon increasing the concentrations of L and H2FDCA,and switching the solvent to pure water,the 1D chain structure of[Cu(HL)_(2)(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(4)was obtained.This shows that changing the solvent and ligand concentrations can affect the structural changes of CPs.In addition,the solid‑state photoluminescence of CPs 1‑4 at room temperature was studied,and their morphological changes were observed via scanning electron microscopy.Density functional theory calculations revealed that the negative charge concentrates on the O and N atoms of the ligand,facilitating ligand‑metal ion coordination.CCDC:2403934,1;2403935,2;2403936,3;2403938,4.展开更多
Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused ...Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.展开更多
This paper addresses the synchronization of follower agents’state vectors with that of a leader in high-order nonlinear multi-agent systems.The proposed low-complexity control scheme employs high-gain observers to es...This paper addresses the synchronization of follower agents’state vectors with that of a leader in high-order nonlinear multi-agent systems.The proposed low-complexity control scheme employs high-gain observers to estimate higher-order synchronization errors,enabling the controller to rely solely on relative output measurements.This approach significantly reduces the dependence on full-state information,which is often infeasible or costly in practical engineering applications.An output feedback control strategy is developed to overcome these limitations while ensuring robust and effective synchronization.Simulation results are provided to demonstrate the effectiveness of the proposed approach and validate the theoretical findings.展开更多
Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and colla...Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.展开更多
This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consen...This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consensus under average dwell time switching.Then sufficient conditions are derived to guarantee the positive consensus.The gain matrices of the control protocol are described using a matrix decomposition approach and the corresponding computational complexity is reduced by resorting to linear programming and co-positive Lyapunov functions.Finally,two numerical examples are provided to illustrate the results obtained.展开更多
In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Mu...In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.展开更多
Organic room-temperature phosphorescence(RTP)materials are promising for bioimaging applications due to their tunable structures,excellent biocompatibility,and long-lived luminescence.However,the development of highly...Organic room-temperature phosphorescence(RTP)materials are promising for bioimaging applications due to their tunable structures,excellent biocompatibility,and long-lived luminescence.However,the development of highly efficient organic RTP materials for aqueous systems remains challenging,as the organic phosphorescence is prone to being quenched by the dissolved oxygen in water.Herein,heteroaromatic carboxylic acids serve as ligand vips to construct a series of host-vip composites with nontoxic,dense EDTA-M(M=Ca,Mg,and Al)coordination polymer in water.These composites exhibit ultra-long pure RTP of vip molecules with phosphorescence quantum yield up to 53%,and lifetime up to 589.7 ms,due to the synergistic effect of dual-network structure:a coordinatively cross-linked network of EDTA-M,and a non-covalent bonded network formed by ligands and water molecules.The phosphorescence intensity is more than three times that of the composite with a single coordination network.Notably,the dual-network configuration can form a rigid and dense structure and block the intrusion of external H_(2)O and O_(2) molecules to avoid phosphorescence quenching in water.As a result,the RTP of the composites remains unchanged after 1 month in water.Furthermore,the nanoparticles fabricated from composites and anionic surfactants can be successfully applied in in vivo imaging of mice for the stable RTP in water.This work provides a novel strategy for the development of high-performance RTP materials in aqueous systems.展开更多
With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier...With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.展开更多
To maximize the profits of power grid operators(GOs),load aggregators(LAs)and electricity customers(ECs),this paper proposes a hierarchical demand response(HDR)framework that considers competing interaction based on m...To maximize the profits of power grid operators(GOs),load aggregators(LAs)and electricity customers(ECs),this paper proposes a hierarchical demand response(HDR)framework that considers competing interaction based on multiagent deep deterministic policy gradient(MaDDPG).The ECs are divided into conventional ECs and the electric vehicles(EVs)which are managed by ECs agent(ECA)and EV agent(EVA)to exploit the flexibility of the HDR framework.Thus,the HDR is a tri-layer model determined by five types of agents engaging in competing interaction to maximize their own profits.To address the limitations of mathematical expression and participation scale in the Stackelberg game within the HDR model,a dynamic interaction mechanism is adopted.Moreover,to tackle the HDR involving various entities,the MaDDPG develops multiple agents to simulation the dynamic competing interactions between each subject as well as solve the problem of continuous action control.Furthermore,MaDDPG adopts soft target update and priority experience replay method to ensure stable and effective training,and makes the exploration strategy comprehensive by using exploration noise.Simulation studies are conducted to verify the performance of the MaDDPG with dynamic interaction mechanism in dealing with multilayer multi-agent continuous action control,compared to the double deep Q network(DDQN),deep Q network(DQN)and dueling DQN.Additionally,comparisons among the proposed HDR with the price based DR(PBDR)and incentive based DR(IBDR)are analyzed to investigate the flexibility of the HDR.展开更多
This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external di...This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.展开更多
Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making p...Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making problems,significantly enhancing swarm intelligence in maneuvering.However,applying MARL to unmanned swarms presents two primary challenges.First,defensive agents must balance autonomy with collaboration under limited perception while coordinating against adversaries.Second,current algorithms aim to maximize global or individual rewards,making them sensitive to fluctuations in enemy strategies and environmental changes,especially when rewards are sparse.To tackle these issues,we propose an algorithm of MultiAgent Reinforcement Learning with Layered Autonomy and Collaboration(MARL-LAC)for collaborative confrontations.This algorithm integrates dual twin Critics to mitigate the high variance associated with policy gradients.Furthermore,MARL-LAC employs layered autonomy and collaboration to address multi-objective problems,specifically learning a global reward function for the swarm alongside local reward functions for individual defensive agents.Experimental results demonstrate that MARL-LAC enhances decision-making and collaborative behaviors among agents,outperforming the existing algorithms and emphasizing the importance of layered autonomy and collaboration in multi-agent systems.The observed adversarial behaviors demonstrate that agents using MARL-LAC effectively maintain cohesive formations that conceal their intentions by confusing the offensive agent while successfully encircling the target.展开更多
Exploring the influence of the coordination environment of single-atom catalysts(SACs)on the electrochemical CO_(2)reduction reaction is vital for assessing the reaction mechanism and structure-performance relationshi...Exploring the influence of the coordination environment of single-atom catalysts(SACs)on the electrochemical CO_(2)reduction reaction is vital for assessing the reaction mechanism and structure-performance relationship.However,it is challenging to engineer the coordination configuration of isolated active metal atoms precisely.Herein,we strategically manipulate the coordination number of the Co-N_(x) configuration by simply changing the order of adding the metal precursor toward improved CO_(2)electrolysis performance.Compared with the symmetric Co-N_(4)coordination,the asymmetric Co-N_(3)coordination leads to reinforced Co-N interaction and downshifted 3d orbital energy toward the Fermi level of the active Co sites,promoting the activation of CO_(2)molecules and the formation of critical intermediate^(*)COOH.The as-designed Co-N_(3)SAC displays excellent Faradaic efficiency(FE)of 98.4%for CO_(2)-to-CO conversion at a low potential of-0.80 V,together with decent FE over a wide potential range(-0.50 V to-1.10 V)and high durability.This study presents an ideal platform to manipulate the coordination number of atomically dispersed metal catalysts and provides a fundamental understanding of coordination configurationperformance correlation for CO_(2)electroreduction.展开更多
Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ...Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.展开更多
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,under project number NBU-FFR-2026-2441-02.
文摘This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.
文摘Interaction is one of the crucial features of multl-agent systems, in which there are two kinds of interaction： agent-to-agent and human-to-agent. In order to unify the two kinds of interaction while designing multi-agent systems, this paper introduces Q language-a scenario description language for designing interaction among agents and humans. Based on Q, we propose an integrating interaction framework system for multi-agent coordination, in which Q scenarios are used to uniformly describe both kinds of interactions. Being in accordance to the characteristics of Q language, the Q-based framework makes the interaction process open and easily understood by the users. Additionally, it makes specific applications of multi-agent systems easy to be established by application designers. By applying agent negotiation in agent-mediated e-commerce and agent cooperation in interoperable information query on the Semantic Web, we illustrate how the presented framework for multi-agent coordination is implemented in concrete applications. At the same time, these two different applications also demonstrate usability of the presented framework and verify validity of Q language.
文摘For estimation group competition and multiagent coordination strategy, this paper introduces a notion based on multiagent group. According to the control domain, it analyzes the multiagent strategy during competition in the macroscopic. It has been adopted in robot soccer and result enunciates that our method does not depend on competition result. It can objectively quantitatively estimate coordination strategy.
文摘This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.
基金supported by the National Natural Science Foundation of China under Grants 61962023,61562029 and 62466019.
文摘This paper presents an adaptive multi-agent coordination(AMAC)strategy suitable for complex scenarios,which only requires information exchange between neighbouring robots.Unlike traditional multi-agent coordination methods that are solved by neural dynamics,the proposed strategy displays greater flexibility,adaptability and scalability.Furthermore,the proposed AMAC strategy is reconstructed as a time-varying complex-valued matrix equation.By introducing a dynamic error function,a fixed-time convergent zeroing neural network(FTCZNN)model is designed for the online solution of the AMAC strategy,with its convergence time upper bound derived theoretically.Finally,the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments.Numerical results indicate that this method can reduce the formation error to the order of 10^(-6)within 1.8 s.
文摘Under solvothermal conditions,1,4‑naphthalenedicarboxylic acid(H_(2)ndc)and 9,9′‑dihexyl‑2,7‑di(pyridin‑4‑yl)fluorene(hfdp)reacted with Co^(2+)ions and Cd^(2+)ions to form two coordination polymers,[Co(hfdp)(ndc)(H2O)]·DMA}n(1)and{[Cd(hfdp)(ndc)(H_(2)O)]·DMA}_(n)(2),respectively(DMA=N,N‑dimethylacetamide).Single‑crystal X‑ray diffraction analyses showed that both complexes 1 and 2 contain similar structures.Topological analysis indicates that complexes 1 and 2 have a{44·62}planar structure.In addition,both complexes reveal good thermal stability and fluorescence sensing performance.They exhibited good sensitivity and selectivity towards 2,4,6‑trinitrophenol(TNP)by fluorescent quenching.The limits of detection of 1 and 2 for TNP were 0.107 and 0.327μmol·L^(-1),respectively.CCDC:2475515,1;2475516,2.
文摘The ionothermal reaction between CuCl_(2),1,4-bis(1,2,4-triazol-1-ylmethyl)benzene(BBTZ),and(NH_(4))_(6)Mo_(7)O_(24) in 1-ethyl-3-methylimidazolium bromide((Emim)Br)led to a new octamolybdate-based coordination polymer(Emim)2[Cu(BBTZ)_(2)(β-Mo_(8)O_(26))](Mo_(8)-CP).Mo_(8)-CP was characterized by elemental analysis,thermogravime-try,IR,powder X-ray diffraction,and single-crystal X-ray diffraction.In Mo_(8)-CP,structural analysis reveals that Cu coordinates with BBTZ ligands to form an interlocked 1D chain.These chains are further bridged by(β-Mo_(8)O_(26))^(4-)to construct a 3D coordination polymer.Notably,(Emim)^(+)acts as a structure-directing agent,occupying the channels of the 3D coordination polymer.Based on this unique structure,the ion exchange properties of Mo_(8)-CP toward rare-earth ions were investigated.It has been found that the luminescent color of the material can be successfully regulat-ed by introducing Eu^(3+)or Tb^(3+)through ion exchange.CCDC:2475110,Mo_(8)-CP.
文摘Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2)O(1)was synthesized as a 2D structure using Coas the metal source,methanol‑water(4∶6,V/V)as the solvent,and specific concentrations of 2,5‑furandicarboxylic acid(H_(2)FDCA)and 1,3,5‑triimidazole benzene(L).Adjusting to pure water and lowering the concentration of L yielded the 1D chain structure of[Co(HL)2(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(2).Using Cu(Ⅱ)as the metal source,methanol/water(9∶1,V/V)as the solvent,and specific concentrations of L and H2FDCA,the 1D chain structure of[Cu(L)(FDCA)(H_(2)O)]·2H_(2)O(3)was synthesized.Upon increasing the concentrations of L and H2FDCA,and switching the solvent to pure water,the 1D chain structure of[Cu(HL)_(2)(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(4)was obtained.This shows that changing the solvent and ligand concentrations can affect the structural changes of CPs.In addition,the solid‑state photoluminescence of CPs 1‑4 at room temperature was studied,and their morphological changes were observed via scanning electron microscopy.Density functional theory calculations revealed that the negative charge concentrates on the O and N atoms of the ligand,facilitating ligand‑metal ion coordination.CCDC:2403934,1;2403935,2;2403936,3;2403938,4.
基金supported in part by the National Natural Science Foundation of China(grants 62203073 and 62573068)the Natural Science Foundation of Chongqing,China(grant CSTB2022NSCQMSX0577)。
文摘Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.
文摘This paper addresses the synchronization of follower agents’state vectors with that of a leader in high-order nonlinear multi-agent systems.The proposed low-complexity control scheme employs high-gain observers to estimate higher-order synchronization errors,enabling the controller to rely solely on relative output measurements.This approach significantly reduces the dependence on full-state information,which is often infeasible or costly in practical engineering applications.An output feedback control strategy is developed to overcome these limitations while ensuring robust and effective synchronization.Simulation results are provided to demonstrate the effectiveness of the proposed approach and validate the theoretical findings.
基金supported by the National Natural Science Foundation of China Youth Fund(No.62101579)。
文摘Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.
基金supported by the National Natural Science Foundation of China(62463007,62463005)the Natural Science Foundation of Hainan Province(625RC710,625MS047)+1 种基金the System Control and Information Processing Education Ministry Key Laboratory Open Funding,China(Scip20240119)the Science Research Funding of Hainan University,China(KYQD(ZR)22180,KYQD(ZR)23180).
文摘This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consensus under average dwell time switching.Then sufficient conditions are derived to guarantee the positive consensus.The gain matrices of the control protocol are described using a matrix decomposition approach and the corresponding computational complexity is reduced by resorting to linear programming and co-positive Lyapunov functions.Finally,two numerical examples are provided to illustrate the results obtained.
文摘In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.
基金supported by the Startup Funds for Introduced Talents of Wuyi University(YJ202304)the National Natural Science Foundation of China(22375044).
文摘Organic room-temperature phosphorescence(RTP)materials are promising for bioimaging applications due to their tunable structures,excellent biocompatibility,and long-lived luminescence.However,the development of highly efficient organic RTP materials for aqueous systems remains challenging,as the organic phosphorescence is prone to being quenched by the dissolved oxygen in water.Herein,heteroaromatic carboxylic acids serve as ligand vips to construct a series of host-vip composites with nontoxic,dense EDTA-M(M=Ca,Mg,and Al)coordination polymer in water.These composites exhibit ultra-long pure RTP of vip molecules with phosphorescence quantum yield up to 53%,and lifetime up to 589.7 ms,due to the synergistic effect of dual-network structure:a coordinatively cross-linked network of EDTA-M,and a non-covalent bonded network formed by ligands and water molecules.The phosphorescence intensity is more than three times that of the composite with a single coordination network.Notably,the dual-network configuration can form a rigid and dense structure and block the intrusion of external H_(2)O and O_(2) molecules to avoid phosphorescence quenching in water.As a result,the RTP of the composites remains unchanged after 1 month in water.Furthermore,the nanoparticles fabricated from composites and anionic surfactants can be successfully applied in in vivo imaging of mice for the stable RTP in water.This work provides a novel strategy for the development of high-performance RTP materials in aqueous systems.
文摘With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.
基金supported by the National Natural Science Foundation of China(No.52477097)the GuangDong Basic and Applied Basic Research Foundation(2023A1515240014)the State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF005).
文摘To maximize the profits of power grid operators(GOs),load aggregators(LAs)and electricity customers(ECs),this paper proposes a hierarchical demand response(HDR)framework that considers competing interaction based on multiagent deep deterministic policy gradient(MaDDPG).The ECs are divided into conventional ECs and the electric vehicles(EVs)which are managed by ECs agent(ECA)and EV agent(EVA)to exploit the flexibility of the HDR framework.Thus,the HDR is a tri-layer model determined by five types of agents engaging in competing interaction to maximize their own profits.To address the limitations of mathematical expression and participation scale in the Stackelberg game within the HDR model,a dynamic interaction mechanism is adopted.Moreover,to tackle the HDR involving various entities,the MaDDPG develops multiple agents to simulation the dynamic competing interactions between each subject as well as solve the problem of continuous action control.Furthermore,MaDDPG adopts soft target update and priority experience replay method to ensure stable and effective training,and makes the exploration strategy comprehensive by using exploration noise.Simulation studies are conducted to verify the performance of the MaDDPG with dynamic interaction mechanism in dealing with multilayer multi-agent continuous action control,compared to the double deep Q network(DDQN),deep Q network(DQN)and dueling DQN.Additionally,comparisons among the proposed HDR with the price based DR(PBDR)and incentive based DR(IBDR)are analyzed to investigate the flexibility of the HDR.
基金supported in part by the Beijing Natural Science Foundation under Grant 4252050in part by the National Science Fund for Distinguished Young Scholars under Grant 62425304in part by the Basic Science Center Programs of NSFC under Grant 62088101.
文摘This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.
基金co-supported by the National Natural Science Foundation of China(Nos.72371052 and 71871042).
文摘Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making problems,significantly enhancing swarm intelligence in maneuvering.However,applying MARL to unmanned swarms presents two primary challenges.First,defensive agents must balance autonomy with collaboration under limited perception while coordinating against adversaries.Second,current algorithms aim to maximize global or individual rewards,making them sensitive to fluctuations in enemy strategies and environmental changes,especially when rewards are sparse.To tackle these issues,we propose an algorithm of MultiAgent Reinforcement Learning with Layered Autonomy and Collaboration(MARL-LAC)for collaborative confrontations.This algorithm integrates dual twin Critics to mitigate the high variance associated with policy gradients.Furthermore,MARL-LAC employs layered autonomy and collaboration to address multi-objective problems,specifically learning a global reward function for the swarm alongside local reward functions for individual defensive agents.Experimental results demonstrate that MARL-LAC enhances decision-making and collaborative behaviors among agents,outperforming the existing algorithms and emphasizing the importance of layered autonomy and collaboration in multi-agent systems.The observed adversarial behaviors demonstrate that agents using MARL-LAC effectively maintain cohesive formations that conceal their intentions by confusing the offensive agent while successfully encircling the target.
基金financially supported by the Program for the Development of Science and Technology of Jilin Province(No.20240101004JJ)the National Natural Science Foundation of China(No.22409165)+4 种基金the National Foreign Experts Program of the Ministry of Human Resources and Social Security(No.Y20240003)the Shaanxi Province Talent Programfinancially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Nos.XDB0600000,XDB0600100,XDB0600200,XDB0600300,XDB0600400)Liaoning Binhai Laboratory(No.LILBLB-2023-04)Dalian Revitalization Talents Program(No.2022RG01)。
文摘Exploring the influence of the coordination environment of single-atom catalysts(SACs)on the electrochemical CO_(2)reduction reaction is vital for assessing the reaction mechanism and structure-performance relationship.However,it is challenging to engineer the coordination configuration of isolated active metal atoms precisely.Herein,we strategically manipulate the coordination number of the Co-N_(x) configuration by simply changing the order of adding the metal precursor toward improved CO_(2)electrolysis performance.Compared with the symmetric Co-N_(4)coordination,the asymmetric Co-N_(3)coordination leads to reinforced Co-N interaction and downshifted 3d orbital energy toward the Fermi level of the active Co sites,promoting the activation of CO_(2)molecules and the formation of critical intermediate^(*)COOH.The as-designed Co-N_(3)SAC displays excellent Faradaic efficiency(FE)of 98.4%for CO_(2)-to-CO conversion at a low potential of-0.80 V,together with decent FE over a wide potential range(-0.50 V to-1.10 V)and high durability.This study presents an ideal platform to manipulate the coordination number of atomically dispersed metal catalysts and provides a fundamental understanding of coordination configurationperformance correlation for CO_(2)electroreduction.
文摘Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.
