两维时间域上的实时程序语言

超稠密计算模型是实时系统的一种抽象模型。该文首先简要介绍一种两维的超稠密时间域及在该域上定义的一种区间逻辑,然后用一个并行模型语言（类Ｏｃｃａｍ语言）讨论用这种逻辑定义并行语言（在超稠密模型中）的时间语义的问题,最后讨论了在这种语义框架中实时系统性质的描述。     

两维时间域上的实时程序语义

超稠密计算模型是实时系统的一种重要抽象模型．该文首先简要介绍一种两维的超稠密时间域及在该域上定义的一种区间逻辑,然后用一个并行模型语言（类Ｏｃｃａｍ 语言）讨论用这种逻辑定义并行语言（在超稠密模型中）的时间语义的问题,最后讨论了在这种语义框架中实时系统性质的描述     

超协调逻辑（Ⅱ）—新超协调逻辑研究

这是关于超协调逻辑研究的第二篇。文中，基于对超协调逻辑应具有一定共性的理解，给出一个超协调逻辑的新定义，并给出在应用研究中提出的三类有代表性的新超协调逻辑，由于它们满足共同的逻辑性质，从而有一定的关系，特别是，它们与非单调逻辑密切有关，因此进一步提出一种相对于超协调性的超完全逻辑的定义与结果。     

处理知识库中不一致性的超决定逻辑研究

本文首先分析了现有的能够处理知识库中不一致性的逻辑的不足，然后定义了超决定结构、模型和语义的概念，具体描述了超决定语义的计算过程，从而给出了一种能够处理知识库中不一致性的逻辑,并对该逻辑进行了一些讨论.     

超协调限制逻辑

本文给出了一阶超协调限制逻辑ＬＰｓ的定义，并证明了它与悖论逻辑（ＬＰ与ＬＰｍ）和限制逻辑（ＣＩＲＣ）的关系，ＬＰ作为一种非单调超协调逻辑具有非单逻辑和超协调逻辑的优点，而用能解决非单调逻辑和超协调逻辑存在的问题，它可作为在不完全与不协调知识下常识推理的形式化，因此它的知识表示中具有广泛的应用。     

概率逻辑系统是与集合代数同态的布尔代数

联结词的本质是命题的运算,只有对所有命题都适用的真值函数才能用于定义联结词.概率逻辑中由于命题的内涵相关性,任何[0,1]上的函数都不能完全适用于任意命题的运算,概率逻辑的联结词不能定义成真值函数.各种算子可以作为一种计算方法使用和研究,但不能代表一个逻辑系统研究系统的性质.概率逻辑系统是概率空间的逻辑表示,是与概率空间中的事件域(集合代数)同态的布尔代数.用事件域上的集合函数精确定义各种联结词,与经典二值逻辑相容,与事实相符,能够在经典逻辑框架内实现概率命题演算.     

悖论逻辑的表演算

悖论逻辑ＬＰ是一个超协调逻辑，发展超协调逻辑（ＬＰ）的目的是使得不会从矛盾推出任一命题，但它有一个主要缺点：就是一些在经典逻辑中有效的推理在ＬＰ中不再有效；极小悖论逻辑ＬＰｍ能克服这个缺点，使得在没有矛盾的直接影响下超协调逻辑等价于经典逻辑．ＬＰ和ＬＰｍ原来都只给出语义定义，虽然已有ＬＰ的证明论，但如何得到一个ＬＰｍ的证明论仍是一个未解问题．本文提出了一种可靠与完全的表演算作为ＬＰ与ＬＰｍ的证明论．     

不协调知识的缺省推理

提出了一个新的缺省推理理论,称为双缺省理论,使得缺省逻辑在四值语义下能够同时处理不协调的知识而不导致扩张的平凡性.为此,定义了命题公式的正变换和负变换,以便分离一个文字与其否定的语义联系.大多数关于缺省逻辑的定理都可以在双缺省逻辑下重建,证明了双缺省逻辑是缺省逻辑在不协调情形下的一般化.提供了一种方法使得超协调逻辑能够获得类似经典逻辑的推理能力.     

广义可能性计算树逻辑和计算树逻辑的关系

广义可能性计算树逻辑(generalized possibilistic computation tree logic,GPo CTL)在不确定性模型检测中扮演着非常重要的角色,但其表达能力还尚未研究全面。为此,讨论了GPo CTL与计算树逻辑(computation tree logic,CTL)表达能力之间的关系。首先定义了区间广义可能性计算树逻辑(interval generalized pos-sibilistic computation tree logic,IGPo CTL),并给出了IGPo CTL公式和CTL公式等价的定义。然后证明了CTL是IGPo CTL的一个真子类,因为IGPo CTL是GPo CTL的一种简单分明化形式,则CTL可看作GPo CTL的一个真子类。此外,还给出了IGPo CTL公式和CTL公式α-等价的定义,并得出了一些更一般的结果。     

超协调限制逻辑的计算复杂性分析

超协调限制逻辑LPc是一种同时具有非单调性和超协调性的非经典逻辑，它可作为在不完全与不协调知识下常识推理的形式化.给出了命题LPc的计算复杂性结果和算法实现，指出LPc是NP完全问题，并给出了将LPc转化为等价的优先限制逻辑的线性时间算法，由于限制逻辑具有实用的实现算法且可用归结方法实现，因而该算法为LPc的实现提供了新的途径.     

Composing and Refining Dense Temporal Logic Specifications

A dense temporal logic development method for the specification, refinement, composition and verification of reactive systems is introduced. A reactive system is specified by a pair consisting of a machine and a condition that indicate the valid computations of this machine. Compositionality is achieved by indicating whether each step is an environment step, a system step, or a communication step. Refinement can be expressed straightforwardly in the logic because the stutter problem is elegantly solved by using the dense structure of the logic. Compositionality enables us to break refinement between complex systems into refinement between small and simple systems. The latter can then be verified by existing proof rules for refinement which are reformulated in our formalism. Received January 1997 / Accepted in revised form April 2000     

Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching

One of the obstacles to widely using first-order logic languages is the fact that relational inference is intractable in the worst case. This paper presents an any-time relational inference algorithm: it proceeds by stochastically sampling the inference search space, after this space has been judiciously restricted using strongly-typed logic-like declarations.We present a relational learner producing programs geared to stochastic inference, named STILL, to enforce the potentialities of this framework. STILL handles examples described as definite or constrained clauses, and uses sampling-based heuristics again to achieve any-time learning.Controlling both the construction and the exploitation of logic programs yields robust relational reasoning, where deductive biases are compensated for by inductive biases, and vice versa.     

A PENALTY-LOGIC SIMPLE-TRANSITION MODEL FOR STRUCTURED SEQUENCES

We study the problem of learning to infer hidden-state sequences of processes whose states and observations are propositionally or relationally factored. Unfortunately, standard exact inference techniques such as Viterbi and graphical model inference exhibit exponential complexity for these processes. The main motivation behind our work is to identify a restricted space of models, which facilitate efficient inference, yet are expressive enough to remain useful in many applications. In particular, we present the penalty-logic simple-transition model, which utilizes a very simple-transition structure where the transition cost between any two states is constant. While not appropriate for all complex processes, we argue that it is often rich enough in many applications of interest, and when it is applicable there can be inference and learning advantages compared to more general models. In particular, we show that sequential inference for this model, that is, finding a minimum-cost state sequence, efficiently reduces to a single-state minimization (SSM) problem. We then show how to define atemporal-cost models in terms of penalty logic, or weighted logical constraints, and how to use this representation for practically efficient SSM computation. We present a method for learning the weights of our model from labeled training data based on Perceptron updates. Finally, we give experiments in both propositional and relational video-interpretation domains showing advantages compared to more general models.     

扩展时段时序逻辑的模型、一致性和推理

给出了扩展时段时序逻辑的时间Petri网（TPN）模型构造方法，在构造模型的同时对时序关系进行一致性检验，在模型的基础上提出了一种时序关系推理算法，这种推理算法基于TPN模型的性质及基本不等式规则，可由一组已知的扩展时段时序关系推出一些未知的扩展时段时序关系，这种推广理算法的优势在于利用了TNP模型的分析技术，减小了推理的时间复杂度比单纯利用不等式规则的推理更直观，也更简单，是一种有效的方法，最后，对扩展时段时序逻辑的TPN模型进行了扩充，增强了其模型和分析的能力。     

An intelligent modeling and analysis method of manufacturing process using the first-order predicate logic

Manufacturing process refers to machining sequence from raw materials to final products. Process plan has important effects on manufacturing process. In general, process designer relies on his experience and knowledge to arrange the process plan. For a complex part, it takes long time and effort to determine process plan. In this paper, an intelligent modeling and analysis method using the first-order predicate logic is proposed to evaluate the manufacturing performance. First, the logic predicates used to represent the process plan are defined according to the machining methods, and the predicate variables are discussed in detail. Consequently, the process plan can be represented in the form of the first-order predicate logic. Second, a type of element model composed of four nodes and four links is put forward in order to construct the process model. All components in this element model are respectively explained, and the mapping relationship between element model and predicate logic is described in detail. According to engineering practices, logic inference rules are suggested and the inference process is illustrated. Hence, the manufacturing process model can be constructed. Third, the process simulation is carried out to evaluate the performance of manufacturing system by using measures such as efficiency, the machine utilization, etc. Finally, a case study is given to explain this intelligent modeling method using the first-order predicate logic.     

用于反应系统的修改时序逻辑

本文提出了一个用于反应系统规范及验证的修改时序逻辑．它包含一个用于显示区分程序执行步同环境执行步的机制．环境的特性可以在系统开发时进行考虑．文中首先给出了程序的一个可复合计算模型──模块转换系统．基于此模型，给出了修改时序逻辑以及它的证明规则．本文提出的方法基于Manna－Pnueli的时序逻辑框架．经典的资源分配问题的例子用于说明此方法．最后给出了并行复合原理，它可以看成是Abadi和LamPort的关于复合假设／保证规范研究工作的具体应用．     

模糊计算树逻辑的符号模型检测

对含有模糊不确定性信息的系统进行模型检测时,状态空间爆炸问题成为了亟待解决的主要问题.将形式化的系统模型用拟布尔公式表示,用多终端二叉决策图来对拟布尔公式进行存储.对模糊计算树逻辑的不动点语义给出了解释和证明,然后给出模糊计算树逻辑的符号化模型检测算法,最后通过一个实例验证算法的正确性.该算法可有效缓解对模糊模型检测验证时的状态空间爆炸问题,并扩展了模型检测的应用范围.     

基于结构的多级逻辑优化

本文提出一个基于结构的多级逻辑优化算法ＭＬＯＢＬＳ，多级组合逻辑网络的优化通过分析名逻辑门的可替代函数，并用简单的替代函数作替代变换完成。算法ＭＬＯＢＬＳ具有良好的逻辑结构重构能力，能得到近似最优的多级逻辑结构。整个优化过程在多级逻辑结构上直接进行，其时／空复杂性较少依赖于多级逻辑结构的基本输入／输出数目。／     

一种五元格值逻辑上命题真度的分布

利用势为5的均匀概率空间的无穷乘积在一种五元格值逻辑系统中引入了公式的真度概念,给出了真度的一些推理规则,证明了全体公式的真度值之集在[0,1]上是稠密的,给出了全体公式真度的表达通式,为在五元格值逻辑系统中建立近似推理理论提供了一种可能的框架。