A join tree probability propagation architecture for semantic modeling

We propose the first join tree (JT) propagation architecture that labels the probability information passed between JT nodes in terms of conditional probability tables (CPTs) rather than potentials. By modeling the task of inference involving evidence, we can generate three work schedules that are more time-efficient for LAZY propagation. Our experimental results, involving five real-world or benchmark Bayesian networks (BNs), demonstrate a reasonable improvement over LAZY propagation. Our architecture also models inference not involving evidence. After the CPTs identified by our architecture have been physically constructed, we show that each JT node has a sound, local BN that preserves all conditional independencies of the original BN. Exploiting inference not involving evidence is used to develop an automated procedure for building multiply sectioned BNs. It also allows direct computation techniques to answer localized queries in local BNs, for which the empirical results on a real-world medical BN are promising. Screen shots of our implemented system demonstrate the improvements in semantic knowledge.     

复杂系统的图模型多智能体协同故障诊断

为解决复杂、不确定系统的故障诊断实时推理问题,提出了基于图模型-多连片贝叶斯网络架构下多智能体协同推理的故障诊断方法。该方法将一个复杂贝叶斯网分割成若干有重叠的贝叶斯子网,使监控网络的单个智能体被抽象为一个拥有局部知识的贝叶斯网,利用成熟的贝叶斯网推理算法可完成智能体的自主推理。随后,通过重叠的子网接口进行多智能体间消息的传播,实现了多智能体协同故障诊断推理。实验结果表明了基于图模型多智能体的协同故障诊断方法的正确性和有效性。     

Understanding the scalability of Bayesian network inference using clique tree growth curves

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a BN, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN's non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.     

Petri网的组合积网及性能分析

Petri网是一种系统描述和分析的工具 .Petri网组合是由小系统的性质获得大系统性质的一种有效的分析方法 .本文提出了 Petri网的组合积运算 ,包括 Petri网的 型组合积运算和 型组合积运算、Petri网的并积运算 ,讨论了保持网的结构性质的条件 ,为复杂大系统的分析提供了新途径     

An algebraic approach to revising propositional rule-based knowledge bases

One of the important topics in knowledge base revision is to introduce an efficient implementation algorithm. Algebraic approaches have good characteristics and implementation method; they may be a choice to solve the problem. An algebraic approach is presented to revise propositional rule-based knowledge bases in this paper. A way is firstly introduced to transform a propositional rule-based knowledge base into a Petri net. A knowledge base is represented by a Petri net, and facts are represented by the initial marking. Thus, the consistency check of a knowledge base is equivalent to the reachability problem of Petri nets. The reachability of Petri nets can be decided by whether the state equation has a solution; hence the consistency check can also be implemented by algebraic approach. Furthermore, algorithms are introduced to revise a propositional rule-based knowledge base, as well as extended logic programming. Compared with related works, the algorithms presented in the paper are efficient, and the time complexities of these algorithms are polynomial.     

Petri网化简技术的动态特性研究

对于大而复杂的Petri网,“节点过多,状态爆炸”一直是阻碍其技术发展的主要问题,因此,如何有效地简化Petri网一直是Petri网研究领域的一个研究热点。将Petri网简化为若干子网是一个很有效的方法。提出了库所指标概念,根据该指标,给出了Petri网的分解定义,并给出了相关算法。在此基础上,依据所给出的算法研究化简技术的动态特性,将子网与原网的特性对应起来进行研究,为复杂系统的Petri网的分析提供了一种有效的途径。     

模糊时间Petri网及其时间推理方法

Petri网是一种用网状图形表示系统模型的方法,它能够从组织结构、控制和管理的角度,精确描述系统中事件(变迁)之间的依赖(顺序)和不依赖(并发)关系。但传统的Petri网理论其不足之处在于:它的分析方法主要是可达树分析法和线性代数描述法。可达树分析法是针对某一个初始标识的,一个新的初始标识就意味着需要重新构造可达状态图;当系统存在较多     

Determination of path identifiers for reliability analysis of a broadcasting network using Petri nets

A Petri net approach for determination of the sets of path identifiers for reliability analysis of a broadcasting network is presented. The firing concept of Petri nets is exploited to give a Petri net interpretation to certain properties of the network. The dynamic behaviour of the system under study is represented in the form of token movements within the model. The various entities of Petri nets are assigned an appropriate interpretation for studying the behaviour of the system at different levels. Corresponding to the source node and each of the terminal nodes, a vector, representing the state of the Petri net interpreted model, is defined by assuming a token initially at the source place and finally at one of the terminal places. The reachability and marking concepts are then used to determine all success states of the system. The developed algorithm has been coded into FORTRAN-77 and the complete package is available. The proposed technique is extremely simple as it uses only vector additions on the columns of the place-to-transition incident matrix.     

M-Petri网的组合性质

提出两类新的组合 M- Petri网 ,讨论了组合网保持网的结构性质的条件 ,从而为 Petri网的分析与综合利用提供了有效的方法     

模糊时间Petri网的时间推理及其在过程监测中的应用

针对传统分析方法的不足,提出用线性逻辑给出模糊时间Petri网描述和时间推理的方法。该方法能清楚地分析模糊时间Petri网的运行行为,具体例子说明了其在系统过程监测和诊断中的应用。     

Hierarchical reachability graph of bounded Petri nets forconcurrent-software analysis

Petri nets have been proposed as a promising tool for modeling and analyzing concurrent-software systems such as Ada programs and communication protocol software. Among analysis techniques available for Petri nets, the most general approach is to generate all possible states (markings) of the system in a form of a so-called reachability graph. However, this conventional reachability graph approach is inefficient or intractable, even for a bounded Petri net, due to state explosion in many practical applications. To cope with this problem, this paper proposes a method for constructing a hierarchically organized state space called the hierarchical reachability graph (HRG). Using the HRG, we obtain necessary and sufficient conditions for reachability and deadlock, as well as algorithms to test whether a given state or marking is reachable from the initial state and whether there is a deadlock state (a state with no successor states)     

Detection of Ada static deadlocks using Petri net invariants

A method is presented for detecting deadlocks in Ada tasking programs using structural; and dynamic analysis of Petri nets. Algorithmic translation of the Ada programs into Petri nets which preserve control-flow and message-flow properties is described. Properties of these Petri nets are discussed, and algorithms are given to analyze the nets to obtain information about static deadlocks that can occur in the original programs. Petri net invariants are used by the algorithms to reduce the time and space complexities associated with dynamic Petri net analysis (i.e. reachability graph generation)     

两类组合Petri网与性能分析*

提出两类新的组合Petri网,讨论组合网保持网的结构性质的条件,从而为Petri网的分析与综合提供了有效的方法.     

线性定常系统的Petri网解耦控制

将Petri网与现代控制理论相结合,应用于连续系统的性能分析如可控性、可观性和稳定性等已日益普遍,但Petri网应用于系统的解耦控制研究很少.提出了广义连续自控网系统的形式化定义,描述了线性定常系统的广义连续自控网系统模型并分析了广义连续自控网系统模型与状态空间描述的等效性.基于状态反馈动态解耦的基本原理,探讨了利用Petri网模型结构实现线性定常系统解耦控制的新方法.该方法采用图的遍历算法,可有效的判断系统的可解耦性以及实现解耦控制律,避免了传统解耦控制方法中计算所需的大量矩阵运算.最后给出了两个具体的应用实例.     

Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines

A vine is a new graphical model for dependent random variables. Vines generalize the Markov trees often used in modeling multivariate distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence. A general formula for the density of a vine dependent distribution is derived. This generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques. Furthermore, the formula allows a simple proof of the Information Decomposition Theorem for a regular vine. The problem of (conditional) sampling is discussed, and Gibbs sampling is proposed to carry out sampling from conditional vine dependent distributions. The so-called canonical vines built on highest degree trees offer the most efficient structure for Gibbs sampling.     

Synthesis of Controllers of Processes Modeled as Colored Petri Nets

This paper presents an adaptation of a supervisory control theory and a supervisor synthesis problem to a class of colored Petri nets. More specifically, the forbidden state control problem with full observation, in which a discrete-event system is modeled as a colored Petri net with a symmetry specification, is investigated. This problem is decidable if the colored Petri net has finite color sets and bounded places. A new algorithm for deriving a controller is presented in detail with a proof of correctness. Unlike conventional algorithms that explore the entire reachable set of states, our algorithm avoids an exhaustive search of the state space by exploiting a symmetry specification. It performs particularly well when applied to large but structured processes with similar components. Furthermore, this approach leads to a representation of controllers which are smaller than those obtained with automaton-based approaches.     

Distributed disk-based algorithms for model checking very large Markov chains

In this paper we present data structures and distributed algorithms for CSL model checking-based performance and dependability evaluation. We show that all the necessary computations are composed of series or sums of matrix-vector products. We discuss sparse storage structures for the required matrices and present efficient sequential and distributed disk-based algorithms for performing these matrix-vector products. We illustrate the effectivity of our approach in a number of case studies in which continuous-time Markov chains (generated in a distributed way from stochastic Petri net specifications) with several hundreds of millions of states are solved on a workstation cluster with 26 dual-processor nodes. We show details about the memory consumption, the solution times, and the speedup. The distributed message-passing algorithms have been implemented in a tool called PARSECS, that also takes care of the distributed Markov chain generation and that can also be used for distributed CTL model checking of Petri nets.