在贝叶斯网络上的概率推理：在单一连接因果树上的确信更新

本文中我们主要讨论在贝叶斯网络中的确信更新算法。首先我们总结了贝叶斯网络的基础，然后详细地描述了算法和数据结构，最后给出了具体实现过程。     

Massively Parallel Probabilistic Reasoning with Boltzmann Machines

We present a method for mapping a given Bayesian network to a Boltzmann machine architecture, in the sense that the the updating process of the resulting Boltzmann machine model probably converges to a state which can be mapped back to a maximum a posteriori (MAP) probability state in the probability distribution represented by the Bayesian network. The Boltzmann machine model can be implemented efficiently on massively parallel hardware, since the resulting structure can be divided into two separate clusters where all the nodes in one cluster can be updated simultaneously. This means that the proposed mapping can be used for providing Bayesian network models with a massively parallel probabilistic reasoning module, capable of finding the MAP states in a computationally efficient manner. From the neural network point of view, the mapping from a Bayesian network to a Boltzmann machine can be seen as a method for automatically determining the structure and the connection weights of a Boltzmann machine by incorporating high-level, probabilistic information directly into the neural network architecture, without recourse to a time-consuming and unreliable learning process.     

Reasoning composite beliefs using a qualitative approach

A Bayesian network is a knowledge representation technique for use in expert system development. The probabilistic knowledge encoded in a Bayesian network is a set of composite hypotheses expressed over the permutation of a set of variables (propositions). Ordering these composite hypotheses according to their a posteriori probabilities can be exponentially hard. This paper presents a qualitative reasoning approach which takes advantage of certain types of topological structures and probability distributions of a Bayesian network to derive the partial ordering of composite hypotheses. Such an approach offers an attractive alternative to reduce the computational complexity of deriving a partial ordering in which consistency is guaranteed.This work is supported in part by a grant to Queens College from the General Research Branch, National Institute of Health under grant No. RR-07064.     

CONSTRUCTION OF BELIEF AND DECISION NETWORKS

We describe a representation and set of inference techniques for the dynamic construction of probabilistic and decision-theoretic models expressed as networks. In contrast to probabilistic reasoning schemes that rely on fixed models, we develop a representation that implicitly encodes a large number of possible model structures. Based on a particular query and state of information, the system constructs a customized belief net for that particular situation. We develop an interpretation of the network construction process in terms of the implicit networks encoded in the database. A companion method for constructing belief networks with decisions and values (decision networks) is also developed that uses sensitivity analysis to focus the model building process. Finally, we discuss some issues of control of model construction and describe examples of constructing networks.     

一种新的多智能体强化学习算法及其在多机器人协作任务中的应用

在多机器人系统中，评价一个机器人行为的好坏常常依赖于其它机器人的行为，此 时必须采用组合动作以实现多机器人的协作，但采用组合动作的强化学习算法由于学习空间 异常庞大而收敛得极慢．本文提出的新方法通过预测各机器人执行动作的概率来降低学习空 间的维数，并应用于多机器人协作任务之中．实验结果表明，基于预测的加速强化学习算法 可以比原始算法更快地获得多机器人的协作策略．     

MULTI-AGENT COOPERATION VIA REASONING ABOUT THE BEHAVIOR OF OTHERS

In multi-agent cooperation, the agents will cooperate efficiently if they can accurately anticipate the behavior of their partners. In this work, we put forward a framework (called as  transpositional thinking principle ) for reasoning about and predicting the behavior of others and propose an approach to planning the cooperation among agents based on the principle. By using the principle, agents can richen their understanding about the behavior patterns of their partners and then infer their partners' actions more and more accurately. The experiments show that the cooperation among agents will be performed more efficiently and at a low cost when agents can anticipate the behavior of others with a high enough accuracy.     

认识逻辑(2):多认识主体的认识逻辑MEL

认识逻辑ＥＬ给出了知识和信念在单一认识主体情形下的逻辑框架。本文将认识逻辑ＥＬ推广到多认识主体的情形，得到了一个可靠并且完备的系统ＭＥＬ。ＭＥＬ继承了ＥＬ的全部性质。ＭＥＬ的一个重要特点是各个认识主体的知识是可以共享的。ＭＥＬ可以作为分布式多ａｇｅｎｔ系统的逻辑基础。     

Logical and algorithmic properties of independence and their application to Bayesian networks

This paper establishes a partial axiomatic characterization of the predicateI(X, Z, Y), to read X is conditionally independent ofY, givenZ. The main aim of such a characterization is to facilitate a solution of theimplication problem namely, deciding whether an arbitrary independence statementI(X, Z, Y) logically follows from a given set of such statements. In this paper, we provide acomplete axiomatization and efficient algorithms for deciding implications in the case where is limited to one of four types of independencies:marginal independencies,fixed context independencies, arecursive set of independencies or afunctional set of independencies. The recursive and functional sets of independencies are the basic building blocks used in the construction ofBayesian networks. For these models, we show that the implication algorithm can be used to efficiently identify which propositions are relevant to a task at hand at any given state of knowledge. We also show that conditional independence is anArmstrong relation [10], i.e., checkingconsistency of a mixed set of independencies and dependencies can be reduced to a sequence of implication problems. This property also implies a strong correspondence between conditional independence and graphical representations: for every undirected graphG there exists a probability distributionP that exhibits all the dependencies and independencies embodied inG.This work was partially supported by the National Science Foundation Grant #IRI-8610155. Graphoids: A Computer Representation for Dependencies and Relevance in Automated Reasoning.     

多智能体系统体系结构及协调机制研究综述

多智能体系统体系结构及其协调机制是多智能体理论中的核心问题,目前在国际上已 经发展了许多方法．本文按照这些方法的研究流派,分别予以介绍、比较并对它们的优缺点 进行评述,最后简要介绍了我们的研究内容．     

一种基于贝叶斯网络的集成的故障定位模型

故障管理是网络管理中最基本也是最重要的功能,目的是保证网络能够连续可靠地运行。故障管理可以分为两个主要的部分:故障检测和故障定位。其中故障定位是核心与难点。文中介绍了一种新的在症状收集时结合被动测试与主动探测,集成了被动诊断对网络正常的通信的影响较小以及主动探测方法可以快速有效地标识故障的优点。在诊断时采用贝叶斯网络来表示症状与故障之间的因果关系,利用不确定推理方法进行故障定位的模型。该模型包括故障推理、逼真度验证、动作选择三个模块。     

多机容错操作系统DFTOS的设计与实现

本文介绍了一个适于三模冗余容错计算机系统的系统管理软件ＤＦＴＯＳ，它是一个多机容错操作系统，具有分布处理和容错计算功能，并且与用户具有良好的接口关系，方便用户对系统的使用和开发。     

基于开放式多智能体结构的分布式自主机器人系统

针对多机器人系统的分布式自主控制,本文首先提出了一种开放式的多智能体结构,给出了 设计原则和技术特点．然后面向真实世界的多机器人实时协作任务,采用多台自主移动机器 人构造了一个多机器人系统, 该系统集成了包括机器人视觉、传感器融合、无线通讯网络 以及基于行为控制等多项技术．最后采用基于行为融合的加权方法,实现了多机器人的编队 控制,实验结果表明了上述体系结构与方案的有效性．     

基于ProActive的容错调度器设计与实现

在分布式计算系统中保证并行应用计算的正确性及提高计算系统中动态资源的利用率是一个重要的研究问题。在原有的基于ProActive的并行计算平台上,引入呼吸通信机制、故障节点发现机制和子任务重新调度机制,设计和实现了一个容错调度系统。实验表明该调度器在部分节点出现故障的情况下,能保证并行计算的正确性,并具有较好的性能。     

Distributed parallel compilation of MSBNs

Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains. Inference with MSBNs can be performed using their compiled representations. The compilation involves moralization and triangulation of a set of local graphical structures. Privacy of agents may prevent us from compiling MSBNs at a central location. In earlier work, agents performed compilation sequentially via a depth‐first traversal of the hypertree that organizes local subnets, where communication failure between any two agents would crush the whole work. In this paper, we present an asynchronous compilation method by which multiple agents compile MSBNs in full parallel. Compared with the traversal compilation, the asynchronous one is robust, self‐adaptive, and fault‐tolerant. Experiments show that both methods provide similar quality compilation to simple MSBNs, but the asynchronous one provides much higher quality compilation to complex MSBNs. Empirical study also indicates that the asynchronous one is consistently faster than the traversal one. Copyright © 2009 John Wiley & Sons, Ltd.     

一种对象化并行计算框架

分布式计算、并行计算、内存计算是目前提高计算性能的关键技术和热点研究领域。在大数据环境下,针对数据型统计分析系统性能劣化明显、不能满足用户使用需求的问题,提出了一种轻量级高性能对象化并行计算架构,研制了该架构的对象服务组件、对象管理服务组件和客户端代理组件,并将该架构和组件在国家电网资产质量监督管理系统中进行了验证应用,其效果表明该框架能大幅提升大数据处理效率。     

基于动态贝叶斯网络的防空作战威胁估计

准确的对战场目标威胁等级进行评估,是战场辅助决策系统的重要环节。通过对威胁估计过程的理解,全面分析了影响威胁等级的评估参数,建立了威胁估计的贝叶斯网络模型,并采用动态贝叶斯网络推理方法进行威胁估计,使目标的各个特征因素以及不同时间片的同一特征因素相互修正,克服了由于专家系统的评估所造成的不确定性和主观性,最后进行了仿真。仿真结果表明,基于动态贝叶斯网络的威胁等级评估算法是一种有效的评估算法,其结果能够比较准确地反映威胁源的真实威胁程度。     

云计算研究

云计算模式是在基础设施即服务（IaaS）、平台即服务（PaaS）、软件即服务（SaaS）、分布式计算、并行计算和网格计算等概念演进并产生的结果。云计算模式是一种全新的计算应用模式,将会成为人们获取服务的主导方式。本文首先介绍云计算在业界中的概念,接着分析云计算和相关计算,最后关于云计算发展的前景进行展望。     

MapReduce框架下结合分布式编码计算的容错算法

随着分布式系统规模扩大及计算复杂度增加,分布式计算的平均故障修复时间和容错计算所产生的通信开销呈现日益上升趋势.结合分布式编码计算和副本冗余技术,提出一种新的容错算法.map节点应用分布式编码计算的思想,将数据冗余分配至多个计算节点创建编码中间结果,降低计算节点在shuffle阶段的数据传输量.reduce节点通过对接...     

一种低费用的协调检查点算法

检查点算法作为一种有效的故障技术及容错手段,已广泛地运用在网格、分布式和云计算系统中。该文提出了一种非阻塞协调检查点算法,该算法增加了系统的可靠性,并允许检查点灵活设置,充分缩减了同步信息数量,加速了检查点形成时间。和典型的相关算法比较,该文提出的算法使用更少的同步控制消息,具有更低的费用,引入同步控制消息的时间复杂度由一般的O（n2）降到O（n）,且同步消息数仅仅为n-1。     

基于垂直FP树的并行频繁项集挖掘

随着数据量的快速增长、数据存储的分散化程度不断提高,对并行分布式数据挖掘算法的需求越来越迫切.文章提出了一种基于垂直FP树的分布式频繁项集挖掘算法DVFP.DVFP采用一种称为垂直FP树(VFP)的格式来存放数据,并同时采用数据并行和任务并行的策略.文章还提出了一种新的序列化方法来对VFP树进行编码,大大减少了处理节点间的通信开销.实验验证DVFP算法在灵活性和处理时间上与现有的分布式算法相比具有较大优势.