Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

A Distributed Algorithm for Parallel Multi-task Allocation Based on Profit Sharing Learning

经由联盟形成的任务分配是在多代理人系统(妈) 的几应用程序域的基本研究挑战，例如资源分配，灾难反应管理等等。怎么以一种分布式的方式分配许多未解决的任务到一些代理人，主要处理。在这篇论文，我们在自我组织、自我学习的代理人之中建议一个分布式的平行多工分配算法。处理状况，我们在二维的房间地理上驱散代理人和任务，然后介绍为寻找它的任务由的一个单个代理人的分享学习的利润(PSL ) 不断自我学习。我们也在代理人之中为通讯和协商介绍策略分配真实工作量到每个 tasked 代理人。最后，评估建议算法的有效性，我们把它与 Shehory 和 Krau 被许多研究人员在最近的年里讨论的分布式的任务分配算法作比较。试验性的结果证明建议算法罐头快速为每项任务形成一个解决的联盟。而且，建议算法罐头明确地告诉我们每个 tasked 代理人的真实工作量，并且能因此为实际控制任务提供一本特定、重要的参考书。

Multi-robot task allocation for exploration

The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.

Semantic-based dynamic positioning mechanism for problem solving in multi-agent systems

In multi-agent systems(MAS),finding agents which are able to service properly in an open and dynamic environment are the key issue in problem solving.However,it is difficult to find agent resources quickly and position agents accurately and complete the system integration by the keyword matching method,due to the lack of clear semantic information of the classical agent model.An semantic-based agent dynamic positioning mechanism was proposed to assist in the system dynamic integration.According to the semantic agent model and the description method,a two-stage process including the domain positioning stage and the service semantic matching positioning stage,was discussed.With this mechanism,proper agents that provide appropriate service to assign sub-tasks for task completion can be found quickly and accurately.Finally,the effectiveness of the positioning mechanism was validated through the in-depth performance analysis in the application of simulation experiments to the system dynamic integration.

Collaboration strategy for software dynamic evolution of multi-agent system

As the ability of a single agent is limited while information and resources in multi-agent systems are distributed, cooperation is necessary for agents to accomplish a complex task. In the open and changeable environment on the Internet, it is of great significance to research a system flexible and capable in dynamic evolution that can find a collaboration method for agents which can be used in dynamic evolution process. With such a method, agents accomplish tasks for an overall target and at the same time, the collaborative relationship of agents can be adjusted with the change of environment. A method of task decomposition and collaboration of agents by improved contract net protocol is introduced. Finally, analysis on the result of the experiments is performed to verify the improved contract net protocol can greatly increase the efficiency of communication and collaboration in multi-agent system.

旅游自动问答系统中多任务问句分类研究

目前旅游产业信息化建设需要构建旅游自动问答系统,其中问句分类是问答系统的重要组成部分,传统问句类别体系角度单一,且传统分类模型对不平衡的问句数据集表现欠佳。针对这一问题,该文从问题主题和问句答案类型两个角度构建了旅游领域的问句类别体系架构,并提出多任务问句分类模型MT-Bert,在BERT上进行多任务训练,并加入自注意力机制,使用Softmax分类器,并设计了多任务融合损失函数。在山西旅游数据集的结果表明,MT-Bert在两种类别体系的微平均F1值分别为97.6%、91.7%,且避免了非平衡数据的预测失败问题,可以有效处理非平衡数据。

基于多种群遗传算法的航天复杂系统测试任务调度

针对航天复杂系统型号较多,传统测试流程与调度设计只能人工定制化排布,效率较低且未有效优化,同时,考虑到航天复杂系统快速测试的迫切需求,提出一种基于多目标遗传算法的航天测试流程自动生成方法。该方法在测试项集合明确的前提下,将测试项抽象为离散事件,以测试总时间和测试资源均衡度为优化目标,充分考虑航天器测试的诸多约束,将其作为遗传算法执行过程中交叉或变异的禁忌项。在初始种群确定后,对测试流程和调度方案进行自动生成和优化。对算例的仿真结果表明,该方法相对于同实验条件下的传统半串行测试方法和单目标优化方法,测试总时间或资源均衡度得到了较大提升。在进一步扩展优化目标和约束项后,该方法可有效提高航天复杂系统测试过程的快速响应能力和可靠性。

面向任务协同的异构多核嵌入式系统实时调度方法

以往的异构多核嵌入式系统实时调度方法由于仅设置了系统实时调度模型的单项参数,导致系统调度时间过长。本文设计了面向任务协同的异构多核嵌入式系统实时调度方法。面向任务协同构建嵌入式系统实时调度模型,计算系统任务节点的传输情况,构建系统实时调度的数学模型,对构建的实时调度模型中的任务数据信息进行汇集,并进行自适应分析处理,以此为基础,设置实时调度模型的时间均衡控制参数和安全性系数,从而实现嵌入式系统的实时调度。通过上述设计,完成对异构多核嵌入式系统实时调度方法的设计。在仿真实验中,与以往的异构多核嵌入式系统实时调度方法相比,本文设计的面向任务协同的异构多核嵌入式系统实时调度方法的调度时间最长仅为5 s,调度时间更短。

软件定义系统技术研究综述

导弹武器装备当前呈现技术密集、任务型号多样、功能复杂化的发展趋势,面对其高速技术迭代的发展需求,传统上采用单型号独立研制的方式存在研制成本高、技术复用率低、结构紧耦合、系统交联复杂等缺点。为此,针对新兴的软件定义系统技术,分别从开放式软件定义系统架构、模型驱动的软件定义系统开发、软件定义系统应用服务管理框架、面向软件定义的载荷快速接入管理、软件定义弹性网络等分项关键技术开展综述研究,介绍相关技术发展情况及存在的不足,分析软件定义系统技术的发展方向,旨在为导弹武器装备研制相关技术难题的突破提供参考。

多任务推荐算法研究综述

单任务推荐算法存在数据稀疏、冷启动和推荐效果不稳定等问题。多任务推荐算法可以将多种类型的用户行为数据和额外信息进行联合建模,从而更好地挖掘用户的兴趣和需求,以提高推荐效果和用户满意度,为解决单任务推荐算法存在的一系列问题提供了新思路。首先,梳理了多任务推荐算法的发展背景与趋势。其次,介绍了多任务推荐算法的实现步骤以及构建原则,并阐述了多任务学习具有数据增强、特征识别、特征互补和正则化效应等优势。然后,对不同共享模式的多任务学习方法在推荐算法中的应用进行了介绍,并对部分经典模型的优缺点及任务之间的关系进行了归纳总结。接着,介绍了多任务推荐算法常用的数据集和评估指标,并阐述了与其他推荐算法在数据集合评估指标方面的区别和联系。最后,指出多任务学习存在负迁移、参数优化冲突、可解释性差等不足,对多任务推荐算法与强化学习、凸函数优化方法、异构信息网络相结合进行了展望。

Multi Agent在分布式测控系统动态任务调度中的实现

Multi Agent实现是基于Multi Agent的分布式测控系统动态任务调度算法实现的关键技术。采用Java作为开发工具,根据Multi Agent的功能,详细论述了Multi Agent在分布式测控系统动态任务调度中的实现。基于Multi Agent的动态任务调度算法根据各主机的负载状态,在系统运行过程中利用移动Agent动态迁移任务。文中详细论述了利用Aglets系统开发和执行移动Agent,从而有效地提高了系统效率,实现了动态任务调度的目标。

基于赋时层次有色Petri网的Multi-Agent调度系统建模

介绍了基于Multi-Agent的分布式环境扫描系统的结构模型.依据赋时层次有色Petri网(HTCP-net)的理论,应用建模、仿真工具CPN Tools建立了基于优先级的任务调度算法和最短等待队列动态负载均衡调度算法的系统调度模型.仿真结果表明,该调度模型有效满足了系统周期性重复访问网站的任务需求.

基于Multi-agent的飞机智能故障诊断与监控系统设计

在对飞机故障进行分析的基础上,以Agent为基本单元,建立了飞机智能故障诊断模型,并对诊断任务的分解、飞机智能诊断与监控系统的结构、智能诊断与监控功能的实现方法等进行了分析和研究,设计了飞机故障诊断与监控系统仿真实验方案,对实验准备,实验过程进行了详细的介绍;仿真实验结果表明,基于智能Agent的飞机故障诊断与监控系统,提高了诊断效率及诊断结果的准确性,增加了诊断系统的灵活性和快速性及对故障的监控能力。

多核处理器中混合关键级任务可调度及半分区划分算法

当前,多数多处理机中混合关键级任务可调度性分析以及半分区调度算法均针对单核利用率展开研究。但由于多核系统任务调度复杂性较高,现有研究结果存在各处理器负载不均衡以及任务可调度性不理想等问题。针对该问题,文中将动态需求边界函数(Dynamic Demand Boundary Function,DDBF)的应用范围扩展至多核处理器系统。根据半分区划分调度算法对DDBF改进,并加入了结转作业和前接作业分析提出了SDDBF(Super Dynamic Demand Boundary Function),可更精确地计算与利用资源。文中基于SDDBF提出了SDA(Stepper Dispatch Algorithm)可调度性分析法与半分区划分算法MCWF(Mixed-Criticality Worist First)。仿真结果表明,相较于AMC(Adaptive Mixed Criticality)、AMC-max以及XU算法,SDA可调度性分析判定提升了5%~10%,相较于WF_MY(Worst First_My)、WF_NEW(Worst First_New)算法,MCWF可使系统在任意关键等级下的CPU(Central Processing Unit)负载具有更良好的均衡性能。

基于多任务学习和单任务学习组合模型的综合能源系统多元负荷预测

针对气象因素对多元负荷变化的灵敏度差异及多元负荷间耦合强度的差异导致多任务学习(multi-tasklearning,MTL)预测模型精度受限的问题,该文提出一种MTL和单任务学习(single-tasklearning,STL)组合的多元负荷预测方法。首先使用基于长短期记忆(long and short-term memory,LSTM)网络的MTL模型提取多元负荷间的耦合信息进行初步预测;然后采用基于前置双重注意力长短期记忆(dual attention before LSTM,DABLSTM)网络的STL模型减少输入噪声进行二次预测;同时将初步的预测值输入STL模型,使得STL模型可以考虑未来的时序信息;最后,通过全连接层对两个模型的预测结果进行融合得到最终的预测结果。实验结果表明,所提组合模型相比单一的MTL和STL模型具有更高的预测精度。

基于HTCP-net的multi-agent调度系统建模与仿真

从企业收集、监测互联网信息的实际需求出发,提出了基于multi-agent的分布式环境扫描系统的结构模型;同时,为满足系统重复采集更新信息的特殊需要,借鉴经典的Min-min启发式调度算法,设计了循环任务最早完成时间调度算法。依据赋时层次有色Petri网(HTCP-net)的理论,应用建模、仿真工具CPN tools建立了调度系统模型。对模型进行仿真,得到任务平均等待时间和各agent等待队列平均长度,与轮转调度和最短等待队列调度模型仿真结果的比较表明,所建立的调度模型既提高了企业用户满意度,又有效满足了系统周期性重复访问网站的任务需求。

村级组织负责人“一肩挑”后权力监督体系的实践与探索——以浙北K区为例

“一肩挑”后基层乡村权力监督体系并没有随着权力高度集中而有所重构,而是继续沿用村“两委”书记、主任分设的“过时”监督格局。基层现行监督体系由于缺乏内部有效监督,以及受“熟人社会”、“搭便车”、村庄派系和“支持-庇护”等因素制约,加之,外部监督力量不足,存在低效(失效)现象。对此,应通过搭建以上级监督、专责监督、内部监督和社会监督等为着力点的多元共监格局,立足于畅通的进退机制、有效的村级监督、明晰的责权边界、快速的问责回应和扎实的上级监督等,塑造更加风清气正的乡村政治生态,为基层治理现代化提供可靠的政治保障。

基于改进NSGA⁃Ⅱ算法的多AGV多任务分配研究

针对自动仓储系统中多AGV的批量任务分配问题,以总任务等待时间、AGV负载均衡指数、总AGV能耗为目标,以AGV和任务的匹配关系为决策变量构建多目标优化数学模型,加入电量约束条件。为克服传统NSGA⁃Ⅱ算法存在的收敛速度慢、种群多样性维护差等不足,提出三种改进策略:改进交叉和变异算子,采用顺序交叉算子、逆序和单点相结合的混合变异算子;改进拥挤度计算公式,提出非线性平均绝对差的拥挤度计算方法;提出从局部和全局双角度调整的动态参数自适应调整策略。最后,设计多AGV多任务分配的仿真实验。实验结果表明:改进的NSGA⁃Ⅱ算法有效解决了多AGV多任务分配问题,同时,所提出的改进策略有效提高了算法的收敛速度、稳定性和鲁棒性。