多agent规划综述*

多Agent规划是智能规划和多Agent系统的交叉领域,随着智能规划领域研究范围的不断扩展和多Agent系统领域研究内容的不断深入,多Agent规划受到了越来越多的关注。有鉴于此,本文对多Agent规划的概念和主要方法进行全面综述。具体内容包括智能规划和多Agent系统的背景介绍、多Agent规划的各种形式化描述方式以及基于规划修复、合并或者马尔可夫决策过程的的分布式规划方法。最后,还给出了多Agent规划的发展趋势。     

基于多代理的供应链协同生产计划模型

供应链是由分布在全球的供应商、制造商、仓库、分销中心和零售商组成的复杂网络。其生产计划具有分布性,自治性,同步性和开放性等特点,使得传统的生产计划方法已经不能适应供应链的计划需求。在分析了多代理技术和供应链系统生产计划特点的基础上,采用智能代理封供应链系统的功能实体和物理实体进行封装,提出了一个基于多代理的供应链系统网络模型,并构建了基于多智能代理的生产计划运行模式。该生产计划模型分为三层：全局生产计划,企业内部子生产计划和各个任务的详细生产计划。它突破了传统生产计划的局限性,从全局规划的角度来整合供应链上的所有资源,消除了不同企业子生产计划所产生的冲突和差异。很好的体现了供应链系统信息共享和资源共享的原则。     

分布式数据库保持隐私挖掘方法

隐私保护是当前数据挖掘领域中一个十分重要的研究问题,其目标是要在不精确访问真实原始数据的条件下,得到准确的模型和分析结果.为了提高对隐私数据的保护程度和挖掘结果的准确性,提出一种基于RSA算法的隐私保护挖掘方法.介绍了公共密钥加密算法RSA的概念,证明了RSA算法的可交换性和加密结果惟一性.然后采用RSA算法,引入了计算中心和混合中心,对原始数据进行了变换和隐藏,实现了保持隐私数据挖掘.最后,对算法的安全性、公平性,有效性和复杂度进行了分析.     

A stochastic algorithm for makespan minimized multi-agent path planning in discrete space

Makespan minimized multi-agent path planning (MAPP) requires the minimization of the time taken by the slowest agents to reach its destination. The resulting minimax objective function is non-smooth and the search for an optimal solution in MAPP can be intractable. In this work, a maximum entropy function is adopted to approximate the minimax objective function. An iterative algorithm named probabilistic iterative makespan minimization (PIMM) is then proposed to approximate a makespan minimized MAPP solution by solving a sequence of computationally hard MAPP minimization problems with a linear objective function. At each iteration, a novel local search algorithm called probabilistic iterative path coordination (PIPC) is used to find a sufficiently good solution for each MAPP minimization problem. Experimental results from comparative studies with existing MAPP algorithms show that the proposed algorithm strikes a good tradeoff between the quality of the makespan minimized solution and the computational cost incurred.     

A multi-agent approach for dynamic channel allocation

We propose a multi‐agent approach for dynamic channel allocation (MA‐DCA) in mobile cellular networks. Our approach assumes that each cell in a cellular network works as an agent that negotiates its bandwidth (channel) requirements with its neighbors to minimize its call drop probability. Using stochastic simulation, we compare our MA‐DCA approach with simple fixed channel allocation (FCA) and dynamic channel borrowing approaches for different call arrival rates, cellular network sizes and number of available channels. The results of our experiments show that the proposed MA‐DCA approach performs better than dynamic channel borrowing and FCA approaches.     

A multi-agent architecture for outsourcing SMEs manufacturing supply chain

In the present, global and competitive market customer's demands are very volatile. It is very difficult for small and medium size enterprises (SMEs) to satisfy their customer with quality product in reasonable price. Large firms are spending huge amount of their budget in generating technology development. To cope with this competitive market, usually, SMEs concentrate on their core expertise and outsource some non-core items or activities. The outsourcing decision (when, what, whom) is very crucial for small-scale industries. Realizing this scenario, in this article, an automated self-adaptive multi-agent system has been proposed. The proposed system will help SMEs to take an appropriate decision to mitigate the uncertainty in supply chain. The developed system exploits the “Outsourcing of operations” feature by its agents to conclude the manufacturing processes faster and reduce the idle time of certain machines with less human intervention. The communication protocol for agents has been described to give an insight of their communication. It was explained how various agents like outsourcing, material planning and supplier selection will come into action at different stages and take appropriate outsourcing decisions. Therefore, this multi-agent architecture will facilitate small scale manufacturing industries to execute their manufacturing processes and complex logistics issues efficiently. The execution of proposed architecture has been described in the simulated case study.     

隐私保护数据挖掘研究进展

近年来隐私保护数据挖掘已经成为数据挖掘的研究热点, 并取得了丰富的研究成果。但是, 随着移动通信、嵌入式、定位等技术的发展与物联网、位置服务、基于位置的社交网络等应用的出现, 具有个人隐私的信息内容更加丰富, 利用数据挖掘工具对数据进行综合分析更容易侵犯个人隐私。针对新的应用需求, 对隐私保护数据挖掘方法进行深入研究具有重要的现实意义。在分析现有的隐私保护数据挖掘方法分类与技术特点的基础上, 提出现有方法并应用于新型分布式系统架构应用系统、高维数据及时空数据等领域存在的挑战性问题, 并指出了今后研究的方向。     

Hierarchical multi-agent planning for flexible assembly of large-scale lunar facilities

Several national space agencies and commercial aerospace companies plan to set up lunar bases with large-scale facilities that rely on multiple lunar robots’ assembly. Mission planning is necessary to achieve efficient multi-robot cooperation. This paper aims at autonomous multi-robot planning for the flexible assembly of the large-scale lunar facility, considering the harsh lunar environment, mission time optimization, and joint actions. The lunar robots and modules are scattered around the mission area without fixed assembly lines. Thus, the traditional assembly planning methods ignoring the optimal selection of modules are unable to handle this problem. We propose a hierarchical multi-agent planning method based on two-stage two-sided matching (HMAP-TTM) to solve this critical problem. First, the distributed planning framework with multi-replica public agents is introduced, ensuring robot plan knowledge consistency through public agents’ communication. Second, the hierarchical task graph (HTG) divides the mission into task layers based on task dependency knowledge. Third, we develop a novel two-stage two-sided matching algorithm. Time-optimal plans emerge from the matching games among public and private agents in each layer of HTG. Agents make decisions in the game based on action knowledge updated during planning. Finally, an assembly mission is presented to prove the method’s effectiveness. The simulation results show that the HMAP-TTM can generate plans with shorter mission time and require smaller communication costs than the baseline methods.     

面向云计算隐私保护的5A问责制建模

针对云计算数据安全的核心问题——隐私安全的保护问题,提出了一种面向云计算隐私保护的5A问责机制。并基于该5A问责机制,对服务提供方的隐私安全策略、租户的隐私需求、云隐私暴露条件和安全场景等进行了精确定义和形式化描述与建模。主要以描述逻辑为基础,重点研究面向语义的云隐私需求描述方法,并对云隐私需求和服务提供方的隐私策略一致性等问题进行检测,避免冲突。在界定并形式化描述云隐私暴露条件和云安全场景的基础上,采用Protégé本体建模工具对云租户的隐私需求和云服务提供方的隐私策略、隐私暴露条件和安全场景等进行建模并检验,验证了形式化建模及其描述的一致性和完整性,为后续5A问责制机制的实现奠定了基础。     

面向多智能体博弈的并行蒙特卡洛树搜索算法研究

蒙特卡洛树搜索算法是一种常用的强化学习算法,博弈过程中动态空间的指数级增长是制约该算法学习效率的因素。基于并行方法对蒙特卡洛树搜索算法进行优化,提出基于胜率估值传递的并行蒙特卡洛树搜索算法。改进后的并行博弈搜索策略框架包含一个主进程和多个子进程,其中子进程用于探索,主进程根据子进程传递的胜率估值数据进行决策。结合多智能体博弈平台Pommerman进行实验验证,与传统的蒙特卡罗树搜索算法相比,并行蒙特卡罗树搜索算法有效提高了资源利用率、博弈胜率及决策效率。     

基于MAS技术的多星协同侦察任务规划系统设计*

基于MAS技术,提出用多agent系统来实现多星协同侦察任务规划系统,建立系统的总体结构,并对其中的关键技术进行分析,给出了基于合同网的多星协作结构,实现了侦察任务分解和任务分配;在单个agent中利用已有规划器SAPA实现单星行动规划。基于MAS建立的任务规划系统具有较好的智能性和自主性。     

基于多Agent系统的组合投资模型

针对多阶段组合投资问题,提出了一个基于多Agent系统的自调节及协同工作的组合投资策略模型.该模型系统中的各个Agent通过通讯共享知识,在求解问题的搜索空间中进行协同搜索,在更短的搜索步长内得到问题的解,极大地提高了系统性能.该模型具有不基于任何股票模型、时间复杂度低以及逼近最优投资策略速度较快等优点,实验证明具有一定的实际意义.     

A local flocking algorithm of multi-agent dynamic systems

In this paper, the local flocking of multi-agent systems is investigated, which means all agents form some groups of surrounding multiple targets with the partial information exchange. For the purpose of realising local multi-flocking, a control algorithm of local flocking is proposed, which is a biologically inspired approach that assimilates key characteristics of flocking and anti-flocking. In the process of surrounding mobile targets through the control algorithm, all agents can adaptively choose between two work modes to depend on the variation of visual field and the number of pursuing agents with the mobile target. One is a flocking pursuing mode which is that some agents pursue each mobile target, the other is an anti-flocking searching mode that means with the exception of the pursing agents of mobile targets, other agents respectively hunt for optimal the mobile target with a closest principle between the agent and the target. In two work modes, the agents are controlled severally via the different control protocol. By the Lyapunov theorem, the stability of the second-order multi-agent system is proven in detail. Finally, simulation results verify the effectiveness of the proposed algorithm.     

保护私有信息的集合交集协议

研究了安全多方计算中的保护私有信息的集合交集问题。在半诚实模型下,基于点积协议设计的两方集合交集协议,复杂度为O(ntp);设计的三方集合交集协议,复杂度为O(2ntp)。给出了协议的正确性理论证明,并对其安全性和复杂度进行了理论分析,性能优于现有协议。最后,给出了协议的推广应用以及不足。     

多智能体系统自适应跟踪控制

基于带有非线性动态的二阶多智能体系统,研究了在有动态领导者条件下的跟踪一致性问题。假设跟随者只能获取邻居智能体的相对状态信息,只有一部分跟随者可以获得领导者的位置和速度信息,领导者的控制输入非零且不被任何一个跟随者可知。在通信拓扑为无向连通图的条件下,为了避免全局信息的不确定性,设计了分布式自适应控制协议。将系统的一致性问题转化为误差系统的一致性问题,通过Lyapunov稳定性理论和矩阵理论分析得到了该协议使系统达到一致的充分条件。最后用仿真例子证明了设计方法的有效性。     

一种个性化移动社交网络轨迹隐私保护方案*

针对移动社交网络迅猛发展带来的发布轨迹隐私泄露问题,提出了一种个性化的轨迹保护方案。根据个体个性化的隐私保护需求差异,对不同个体采用了不同的保护准则,这样可以解决传统隐私保护下“过度保护”及轨迹效用低等问题。给出k敏感轨迹匿名和(k,p)敏感轨迹匿名等重要的隐私保护定义,并利用Trie树的构造、剪枝、重构等技术实现了个体的个性化隐私保护。最后,通过在真实数据集上的实验分析,证明该个性化方案比现存隐私保护方案在轨迹位置损失率方面性能优,计算延时较低和效率高。     

Leader-following consensus for uncertain second-order nonlinear multi-agent systems

This paper studies the leader-following consensus problem for a class of second-order nonlinear multi-agent systems subject to linearly parameterized uncertainty and disturbance. The problem is solved by integrating the adaptive control technique and the adaptive distributed observer method. The design procedure is illustrated by an example with a group of Van der Pol oscillators as the followers and a harmonic system as the leader.     

Distributed output regulation for linear multi-agent systems with communication delays

In this paper, a distributed output regulation approach is presented for the cooperative control of linear multi-agent systems in the presence of communication delays. Both dynamic state and output feedback control laws are designed for achieving the property of output regulation. Sufficient conditions for the existence of these control laws are provided in terms of linear matrix inequalities. Simulation results are given to support the efficiency of the proposed distributed output regulation approach.     

A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements

Most existing flocking algorithms rely on information about both relative position and relative velocity among neighbouring agents. In this article, we investigate the flocking problem with only position measurements. We propose a provably-stable flocking algorithm, in which an output vector is produced by distributed filters based on position information alone but not velocity information. Under the assumption that the initial interactive network is connected, the flocking algorithm not only can steer a group of agents to a stable flocking motion, but also can preserve the connectivity of the interactive network during the dynamical evolution. Moreover, we investigate the flocking algorithm with a virtual leader and show that all agents can asymptotically attain a desired velocity even if only one agent in the team has access to the information of the virtual leader. We finally show some numerical simulations to illustrate the theoretical results.