自组织分治求解分布式约束优化问题

分布式约束优化问题(DCOP)是在大规模、开放、动态网络环境中的优化问题,在计算网格、多媒体网络、电子商务、企业资源规划等领域中都有广泛应用.除了具有传统优化问题的非线性、约束性等特点,DCOP还具有动态演化、信息区域化、控制局部化、网络状态异步更新等特点.寻求一种解决DCOP的大规模、并行、具有智能特征的求解方法已成为一个具有挑战性的研究课题.目前已提出多种求解DCOP的算法,但大多不是完全分散的算法,存在集中环节,需要网络的全局结构作为输入,不适合处理由规模巨大、地理分布、控制分散等因素导致的全局结构难以获取的分布式网络.针对该问题,提出一个基于自组织行为的分治策略求解DCOP.在不具有全局网络知识的情况下,分布在网络中的多个自治Agent基于局部感知信息、采用自组织的方式协作求解.与已有算法相比,它是一个完全分散式算法,并在求解效率和求解质量方面都展现出很好的性能.     

DCSP和DCOP求解研究进展

分布式约束满足问题（DCSP）和分布式约束最优问题（DCOP）的研究是分布式人工智能领域的基础性工作。本文首先介绍了卿和DCOP的形式化描述及对实际应用问题的建模方法。在DCSP和DCOP的求解中,通常对问题要进行限制和要求,同时要满足分布性、异步性、局部性、完备性的原则。异步回溯（ABT）、异步弱承诺搜索（AWC）和分布式逃逸（DB）算法是求解DCSP的有代表性的算法;DCSP算法对DCOP求解产生了影响,但由DCSP一般化到DCOP的算法,仅适用于解决部分特定的问题,DCOP的最优、异步算法有异步分布式约束最优算法（A—dopt）和最优异步部分交叉算法（OptAPO）。本文讨论了上述算法的性能。相关的研究工作在多局部变量的处理、超约束DCSP、算法性能度量、通信的保密等方面进行了扩充,在对问题本身的研究、建模方法学、算法、与其他方法的结合以及拓展应用领域等方面仍有许多问题需要进一步研究。     

基于合作仲裁求解分布式约束优化问题的研究

MAS中许多分布式推理问题都可以建模为分布式约束优化问题(DCOP).在这里,我们把分布式会议调度DMS(Dis-tributed Meeting Scheduling)问题映射为DCOP,基于合作仲裁进行求解,并把结果与另一个DCOP算法比较.考虑到完全解决方案的时间复杂性,我们把局部约束图转换为伪树,加速了搜索速度,从而在较短的时间找到最优解决方案.     

一种动态分布式约束优化问题协同求解算法

多Agent协作过程中的许多问题都可在分布式约束优化问题(DCOP)框架下建模,但多局限于规划问题,且一般需Agent具有完全、准确收益函数.针对DCOP局限性,定义动态分布式约束优化问题(DDCOP),分析求解它的两个关键操作:Exploration和Exploitation,提出基于混沌蚂蚁的DDCOP协同求解算法(CA-DDCOP).该算法借鉴单只蚂蚁的混沌行为和蚁群的自组织行为,实现Exploration和Exploitation,根据玻尔兹曼分布,建立平衡Exploration和Exploitation的协同方法.通过多射频多信道无线AdHoc网络的信道分配验证该算法的有效性.     

低约束密度分布式约束优化问题的求解算法

多Agent协作过程中的许多挑战都可以建模为分布式约束优化问题.针对低约束密度的分布式约束优化问题,提出了一种基于贪婪和回跳思想的求解算法.在该算法中,各Agent基于贪婪原则进行决策,能够利用低约束密度问题中大量赋值组合代价为0这一特点来加快求解速度.同时,Agent间的回跳机制可以在贪婪原则陷入局部最优时保证算法的完全性.相对于已有主流算法,该算法可以在保持多项式级别的消息长度/空间复杂度的前提下,以较少的消息数目求解低约束密度的分布式约束优化问题.给出了算法关键机制的正确性证明,并通过实验验证了算法的上述性能优势.     

有限资源环境下的分层分布式体系结构研究

1 引言随着网络技术、分布式理论的发展,人工智能进入了一个新的阶段。人们从不同的方面对分布式人工智能的理论、结构、应用进行了深入的研究。其中分布式问题求解(DPS)、并行人工智能(PAI)以及多Agent系统(MAS)是分布式人工智能的三个主要研究领域。DPS的研究一般是面向任务的,在任务间进行分配、协调,从而生成大量的专家系统和基于知识的系统,在粗粒度上研究控制和数据分布。MAS的研究在近十年中得到较快的发展,当前已经出现了基于Agent的系统和开发Agent的工具和语言等。DPS和MAS的研     

一种新的基于复合弹簧网络的多Agent系统分布式问题求解方法

当多Agent系统（MAS）中Agent之间存在多种复杂的随机的社会交互行为时，当各Agent表现出不同程度的自治性和理性时，难以用现有的方法描述和求解MAS问题，即使对仅仅存在竞争和合作这两种社会交互行为，并且不考虑Agent之间自治程度的本质性差异时，现有的基于结盟的MAS问题求解算法也具有极高的计算复杂性，该文提出一种新的复合弹簧网络模型和方法，利用分布式弹性动力学方程，将MAS分布式问题求解过程转变对应的复合弹簧网络形变过程，这种模型和方法能够处理各种社会交互行为以及Agent不同程度的自治性，分析和仿真实验表明，在计算复杂性和适用性等许多方面，该文的分布并行算法优于文献[7,8]的Shehory-Kraus算法。     

基于局部并行搜索的分布式约束优化算法框架

针对当前局部搜索算法在求解大规模、高密度的分布式约束优化问题（DCOP）时,求解困难且难以跳出局部最优取得进一步优化等问题,提出一种基于局部并行搜索的分布式约束优化算法框架（LPOS）,算法中agent通过自身的取值并行地搜索局部所有邻居取值来进一步扩大对解空间的搜索,从而避免算法过早陷入局部最优。为了保证算法的收敛性与稳定性,设计了一种自适应平衡因子K来平衡算法对解的开发和继承能力,并在理论层面证明了并行搜索优化算法可以扩大对解空间的搜索,自适应平衡因子K可以实现平衡目的。综合实验结果表明,基于该算法框架的算法在求解低密度和高密度DCOP时性能都优于目前最新的算法。特别是在求解高密度DCOP中有显著的提升。     

分布式约束满足问题及其在MAS任务分配中的应用*

针对多智能体系统(MAS)任务分配问题中多个任务与MAS两者的分布式特征,将任务分配问题形式化为分布式约束满足问题(DCSP)进行求解,分别建立了以任务为中心和以agent为中心两种MAS任务分配模型,基于改进的DCSP分布式并行求解算法,提出了基于DCSP的MAS任务分配问题求解框架。该方法适合求解agent间通信有随机延迟以及agent间存在多约束的问题,应用实例的求解表明了其实用性与有效性。     

Agent组织研究进展

Agent理论和技术的研究自20世纪70年代末出现以来发展很快,研究工作从个体Agent模型和思维状态理论扩展到群体Agent合作求解,取得了一系列进展.近年来,Agent组织的研究越来越引起重视,作为多Agent系统(MAS)的一种求解结构,基于Agent组织的问题求解可以有效地降低求解难度和Agent之间的交互复杂性.综述了Agent组织近年的研究进展,介绍了Agent组织模型、MAS思维状态模型、规范化MAS和Agent联盟等方面的研究成果,并指出了今后的研究方向.     

Algorithms for Distributed Constraint Satisfaction: A Review

When multiple agents are in a shared environment, there usually exist constraints among the possible actions of these agents. A distributed constraint satisfaction problem (distributed CSP) is a problem to find a consistent combination of actions that satisfies these inter-agent constraints. Various application problems in multi-agent systems can be formalized as distributed CSPs. This paper gives an overview of the existing research on distributed CSPs. First, we briefly describe the problem formalization and algorithms of normal, centralized CSPs. Then, we show the problem formalization and several MAS application problems of distributed CSPs. Furthermore, we describe a series of algorithms for solving distributed CSPs, i.e., the asynchronous backtracking, the asynchronous weak-commitment search, the distributed breakout, and distributed consistency algorithms. Finally, we show two extensions of the basic problem formalization of distributed CSPs, i.e., handling multiple local variables, and dealing with over-constrained problems.     

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

This article presents an asynchronous algorithm for solving distributed constraint optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities for communication, leading to an important speed-up. While feedback can be sent in ADOPT by COST messages only to one predefined predecessor, our extension allows for sending such information to any relevant agent. The concept of valued nogood is an extension by Dago and Verfaille of the concept of classic nogood that associates the list of conflicting assignments with a cost and, optionally, with a set of references to culprit constraints. DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. To be efficient, ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering. To exploit such DFS trees it is now sufficient to ensure that they exist. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

The distributed constraint satisfaction problem: formalization andalgorithms

We develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in distributed artificial intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems     

Benchmarking hybrid algorithms for distributed constraint optimisation games

In this paper, we consider algorithms for distributed constraint optimisation problems (DCOPs). Using a potential game characterisation of DCOPs, we decompose eight DCOP algorithms, taken from the game theory and computer science literatures, into their salient components. We then use these components to construct three novel hybrid algorithms. Finally, we empirical evaluate all eleven algorithms, in terms of solution quality, timeliness and communication resources used, in a series of graph colouring experiments. Our experimental results show the existence of several performance trade-offs (such as quick convergence to a solution, but with a cost of high communication needs), which may be exploited by a system designer to tailor a DCOP algorithm to suit their mix of requirements.     

基于移动Agent与P/S混合模式的分布对象中间件异步通信研究

针对分布对象中间件异步通信中时间、空间和流程的三刻度解耦问题进行了研究,在分析了当前分布对象中间件通信机制所存在的问题基础上,基于CORBA分布环境引入了P/S发布订阅系统,构建了基于P/S模式的CORBA通信体系.同时在PIS中间件代理体系中,引入移动Agent代理技术构建了基于Agent与P/S混合模式的异步通信算法,解决了基于中间代理的分布对象中间件异步通信的三刻度解耦问题.