A Novel Multi-agent Formation Control Law With Collision Avoidance

In this paper a stable formation control law that simultaneously ensures collision avoidance has been proposed.It is assumed that the communication graph is undirected and connected.The proposed formation control law is a combination of the consensus term and the collision avoidance term(CAT).The first order consensus term is derived for the proposed model,while ensuring the Lyapunov stability.The consensus term creates and maintains the desired formation shape,while the CAT avoids the collision.During the collision avoidance,the potential function based CAT makes the agents repel from each other.This unrestricted repelling magnitude cannot ensure the graph connectivity at the time of collision avoidance.Hence we have proposed a formation control law,which ensures this connectivity even during the collision avoidance.This is achieved by the proposed novel adaptive potential function.The potential function adapts itself,with the online tuning of the critical variable associated with it.The tuning has been done based on the lower bound of the critical variable,which is derived from the proposed connectivity property.The efficacy of the proposed scheme has been validated using simulations done based on formations of six and thirty-two agents respectively.

Nonlinear Control for Multi-agent Formations with Delays in Noisy Environments

In this paper, we investigate the nonlinear control problem for multi-agent formations with communication delays in noisy environments and in directed interconnection topologies. A stable theory of stochastic delay differential equations is established and then some sufficient conditions are obtained based on this theory, which allow the required formations to be gained at exponentially converging speeds with probability one for time-invariant formations, time-varying formations, and time-varying formations for trajectory tracking under a special"multiple leaders" framework. Some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

The coupling control of biological precursors and environmental factors onβ-carotane enrichment in alkaline lacustrine source rocks:A case study from the Fengcheng formation in the western Junggar Basin,NW China

The organic-rich mudstones and dolostones of the Permian Fengcheng Formation(Fm.)are typically alkaline lacustrine source rocks,which are typified by impressively abundantβ-carotane.Abundant β-carotane has been well acknowledged as an effective indicator of biological sources or depositional environments.However,the specific biological sources of β-carotane and the coupling control of biological sources and environmental factors on the enrichment of β-carotane in the Fengcheng Fm.remains obscure.Based on a comprehensive investigation of the bulk,molecular geochemistry,and organic petrology of sedimentary rocks and the biochemistry of phytoplankton in modern alkaline lakes,we proposed a new understanding of the biological precursors of β-carotane and elucidated the enrichment mechanism of β-carotane in the Fengcheng Fm.The results show that the biological precursors crucially control the enrichment of β-carotane in the Fengcheng Fm.The haloalkaliphilic cyanobacteria are the primary biological sources of β-carotane,which is suggested by a good positive correlation between the 2-methylhopane index,7-+8-methyl heptadecanes/C_(max),C_(29%),and β-carotane/C_(max)in sedimentary rocks and the predominance of cyanobacteria with abundantβ-carotene in modern alkaline lakes.The enrichment of β-carotane requires the reducing condition,and the paleoredox state that affects the enrichment of β-carotane appears to have a threshold.The paleoclimate conditions do not considerably impact the enrichment of β-carotane,but they have some influence on the water's paleosalinity by affecting evaporation and precipitation.While it does not directly affect the enrichment of β-carotane in the Fengcheng Fm.,paleosalinity does have an impact on the cyanobacterial precursor supply and the preservation conditions.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

A survey on multi-agent reinforcement learning and its application

Multi-agent reinforcement learning(MARL)has been a rapidly evolving field.This paper presents a comprehensive survey of MARL and its applications.We trace the historical evolution of MARL,highlight its progress,and discuss related survey works.Then,we review the existing works addressing inherent challenges and those focusing on diverse applications.Some representative stochastic games,MARL means,spatial forms of MARL,and task classification are revisited.We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications.We also address critical operational aspects,such as hyperparameter tuning and computational complexity,which are pivotal in practical implementations of MARL.Afterward,we make a thorough overview of the applications of MARL to intelligent machines and devices,chemical engineering,biotechnology,healthcare,and societal issues,which highlights the extensive potential and relevance of MARL within both current and future technological contexts.Our survey also encompasses a detailed examination of benchmark environments used in MARL research,which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios.In the end,we give our prospect for MARL and discuss their related techniques and potential future applications.

CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.

The Lower Cambrian Xiaoerbulake Formation in the Tarim Basin as a potential carbonate source rock

The oil and gas exploration of the Middle and Lower Cambrian in the Tarim Basin reveals widely distributed source rocks with the Yuertusi Formation being recognized as high-quality source rocks that are distributed in a rather small range.The Xiaoerbulake Formation that is right under the Yuertusi Formation has also been eyed as potential high-quality source rocks and is studied through analyses focusing on the stratigraphic development,the abundance,type,and maturity of organic matter,and the paleoproductivity of a dark-colored algae dolomite within the formation.The results show that the dolomite is rich in organic matter of mainly types I and II kerogens.Although reached the high mature to over-mature stage,the dolomite was deposited in an anoxic sedimentary environment featuring a high paleoproductivity level and a high organic carbon burial efficiency,quite favorable for the development of high-quality source rocks.The study provides material evidence to the Middle-Lower Cambrian subsalt source rock-reservoir-caprock combination model for the Tarim Basin.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.

Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems

This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent sys-tems.New hyperbolic tangent function-based protocols are pro-posed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed commu-nication topologies.These new protocols not only provide an explicit upper-bound estimate for the settling time,but also have a user-prescribed bounded control level.In addition,compared to some existing results based on the saturation function,the pro-posed approach considerably simplifies the protocol design and the stability analysis.Illustrative examples and an application demonstrate the effectiveness of the proposed protocols.

Sema3A secreted by sensory nerve induces bone formation under mechanical loads

Bone formation and deposition are initiated by sensory nerve infiltration in adaptive bone remodeling. Here, we focused on the role of Semaphorin 3A(Sema3A), expressed by sensory nerves, in mechanical loads-induced bone formation and nerve withdrawal using orthodontic tooth movement(OTM) model. Firstly, bone formation was activated after the 3rd day of OTM,coinciding with a decrease in sensory nerves and an increase in pain threshold. Sema3A, rather than nerve growth factor(NGF),highly expressed in both trigeminal ganglion and the axons of periodontal ligament following the 3rd day of OTM. Moreover, in vitro mechanical loads upregulated Sema3A in neurons instead of in human periodontal ligament cells(hPDLCs) within 24 hours.Furthermore, exogenous Sema3A restored the suppressed alveolar bone formation and the osteogenic differentiation of hPDLCs induced by mechanical overload. Mechanistically, Sema3A prevented overstretching of F-actin induced by mechanical overload through ROCK2 pathway, maintaining mitochondrial dynamics as mitochondrial fusion. Therefore, Sema3A exhibits dual therapeutic effects in mechanical loads-induced bone formation, both as a pain-sensitive analgesic and a positive regulator for bone formation.

Early-diverging Titanosauriform(Dinosauria,Sauropoda)Teeth from the Lower Cretaceous Yixian Formation of Southeastern Inner Mongolia,Northeast China

Three eusauropod teeth(SDUST-V1064,PMOL-AD00176,PMOL-ADt0005)are reported from the Lower Cretaceous Yixian Formation of Ningcheng,southeastern Inner Mongolia,China.Two of them(SDUST-V1064,PMOL-AD00176)are assigned to early-diverging titanosauriforms in having slightly mesiodistal expansion at the base of the tooth crown,a slenderness index value>2.0 and<4.0,and D-shaped cross section.Furthermore,SDUST-V1064 and PMOL-AD00176 are referred as an Euhelopus-like titanosauriform on the basis of having a sub-circular boss on the lingual surface and an asymmetrical crown-root margin which slants apically,respectively.CT scan data of SDUST-V1064 reveals new dental information of early-diverging titanosauriforms,for example,the enamel on the labial side thicker than that on the lingual side,an enamel/dentine ratio of 0.26 and a boss present on the lingual side of the dentine of the crown.

TBAB hydrate formation and growth in a microdevice under static and dynamic conditions

The natural gas hydrate has become one of the most promising future green energy sources on the earth.The natural gas hydrates mostly exist in the sediments with porous structure, so a solid understanding of the hydrate formation and growth processes in the porous medium is of significance for the exploitation of natural gas hydrate. The micro-packed bed device is one of the efficient microfluidic devices in the engineering field, but it has been rarely used for the hydrate-based research. In this study, a transparent micro-packed bed device filled with glass beads was developed to mimic the porous condition of sediments, and used to in-situ visualize the hydrate formation and growth habits in the pore spaces under both static and dynamic conditions. For the static experiment, two types of hydrate growth patterns in porous medium were observed and identified in the micro-packed bed device, which were the graincoating growth and pore-filling growth. For the dynamic condition, the hydrate formation, growth,distribution habits and hydrate blockage phenomena in the pore spaces were in-situ visually captured.The impacts of flowrate and subcooling on the pressure variation of the micro-packed bed and the duration of the hydrate growth under dynamic flow condition in pores were in-situ monitored and analyzed. The higher flowrate could result in the faster hydrate growth and more severe blockage in pores, but the effect of subcooling condition might be less significant at the high flowrate.

Facies development and sedimentology of the Middle Miocene carbonates of the Raghama Formation, northeastern Saudi Arabia

Ragahama Formation comprises a siliciclastic continental deposits followed by marine carbonates, representing prograding alluvial fans from adjacent high hinterlands seaward into lagoons and fringing reef environments. The present work aimed to document the facies development and sedimentology of the Raghama carbonates exposed along the eastern coastal plain of the Red Sea, northwestern Saudi Arabia. Four stratigraphic sections were measured and sampled(D1–D4) and thin sections and major and trace element analyses were prepared and applied for petrographic and geochemical approaches. The carbonates were subdivided into three successive fore-reef, reef-core, and back-reef depositional facies. Sandy stromatolitic boundstone, microbial laminites, dolomitic ooidal grainstone, bioclastic coralline algal wackestone, sandy bioclastic wackestone, and coral boundstones were the reported microfacies types. Petrographic analysis reveals that the studied carbonates were affected by dissolution, dolomitization, and aggrading recrystallization, which affects both the original micrite matrix and grains or acts as fracture and veinlet filling leading to widespread vuggy and moldic porosity. No evidence of physical compaction, suggesting rapid lithification and recrystallization during early diagenesis and prior to substantial burial and intensive flushing by meteoric waters. Most of the original microstructure of corals were leached and destructed. This is indicated by the higher depletion in Sr and Ca levels and increase in Mg,Na, Fe, and Mn levels, especially in section D1, in comparison with the worldwide carbonates.

Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns

Positional information encoded in spatial concentration patterns is crucial for the development of multicellular organisms.However,it is still unclear how such information is affected by the physically dissipative diffusion process.Here we study one-dimensional patterning systems with analytical derivation and numerical simulations.We find that the diffusion constant of the patterning molecules exhibits a nonmonotonic effect on the readout of the positional information from the concentration patterns.Specifically,there exists an optimal diffusion constant that maximizes the positional information.Moreover,we find that the energy dissipation due to the physical diffusion imposes a fundamental upper limit on the positional information.