Multiple inheritance systems with exceptions

This paper discusses some problems that arise in knowledge-based intelligent systems using property inheritance as the principal inference mechanism. A few methods that deal with those problems are presented here. They are divided into three groups: those that are based on different forms of non-monotonic logics, algebraic structures, and connectionist networks. Implementational issues concerning inheritance systems are considered with the special emphasis put on parallel methods of computation.     

Modelling multiple inheritance with colimits

A categorical semantics of multiple inheritance is presented, in which the inheritance relations between program entities are represented as commutative diagrams in a suitable category, and multiple inheritance is then modelled with colimits of such diagrams. It is shown that the informal semantics of multiple inheritance used in existing object-oriented programming languages conforms to, or is consistent with, this model.     

Hybridizing nonmonotonic inheritance with theorem proving

Theorem proving techniques and inheritance are often thought of as quite different reasoning styles. This paper concentrates on similarities between the two. It suggests a criterion for when an inheritance consequence relation can be integrated coherently with a stronger logical consequence relation, and develops in some detail one way in which a nonmonotonic inheritance network could be integrated with a Gentzen-style theorem prover for the Boolean connectives.     

用Excel解方程组

利用Excel中矩阵函数的强大的数据处理功能,用矩阵逆函数MINVERSE,克拉默法则,规化求解命令,和EXCEL中的IF函数和行列式值MDETERM函数四种方法,均可以方便的求出方程组的解.本文通过两个实例对上述方法做了详细介绍,并给出了题解图例.     

基于子空间的可解释性多变量异常检测

为了解决在多维特征数据中,部分异常点被分散的特征空间所掩盖而无法检出的问题,以及缓解当前异常检测方法的结果可解释性差或不具有可解释性的状况,提出了基于子空间的可解释性多变量异常检测算法;首先在多维特征空间中,对每一个维度的特征进行分布检验,在此基础上为每一个对象选择出一个特征空间的集合,进而为每个对象计算出异常值分数;在此过程中,高效利用算法的中间过程产物,来对算法结果加以解释,改善使用者对数据的理解;使用真实数据集,对算法进行了验证,实验结果表明其具有较好的准确性和运行时间,并很好的解释了异常点的异常性。     

Attributed graph transformation with node type inheritance

The aim of this paper is to integrate typed attributed graph transformation with node type inheritance. Borrowing concepts from object oriented systems, the main idea is to enrich the attributed type graph with an inheritance relation and a set of abstract nodes. In this way, a node type inherits the attributes and edges of all its ancestors. Based on these concepts, it is possible to define abstract productions, containing abstract nodes. These productions are equivalent to a number of concrete productions, resulting from the substitution of the abstract node types by the node types in their inheritance clan. Therefore, productions become more compact and suitable for their use in combination with meta-modelling. The main results of this paper show that attributed graph transformation with node type inheritance is fully compatible with the existing concept of typed attributed graph transformation.     

Network anomaly detection with incomplete audit data

With the ever increasing deployment and usage of gigabit networks, traditional network anomaly detection based Intrusion Detection Systems (IDS) have not scaled accordingly. Most, if not all IDS assume the availability of complete and clean audit data. We contend that this assumption is not valid. Factors like noise, mobility of the nodes and the large amount of network traffic make it difficult to build a traffic profile of the network that is complete and immaculate for the purpose of anomaly detection. In this paper, we attempt to address these issues by presenting an anomaly detection scheme, called SCAN (Stochastic Clustering Algorithm for Network Anomaly Detection), that has the capability to detect intrusions with high accuracy even with incomplete audit data. To address the threats posed by network-based denial-of-service attacks in high speed networks, SCAN consists of two modules: an anomaly detection module that is at the core of the design and an adaptive packet sampling scheme that intelligently samples packets to aid the anomaly detection module. The noteworthy features of SCAN include: (a) it intelligently samples the incoming network traffic to decrease the amount of audit data being sampled while retaining the intrinsic characteristics of the network traffic itself; (b) it computes the missing elements of the sampled audit data by utilizing an improved expectation–maximization (EM) algorithm-based clustering algorithm; and (c) it improves the speed of convergence of the clustering process by employing Bloom filters and data summaries.     

用连杆机构几何约束求解

在这篇文章里,我们引入连杆机构作为新的工具,且证明这是完备的,也就是说,所有能构造性描述的图形能被连杆机构作出,这一类包括了所有只含距离约束的约束问题.作为一个应用,我们说明了超出Owen和Hoffmann的三角分解方法之外的最简单的约束图能被转化为纯几何构造形式.为了求解起源于连杆构造的方程,我们提出了一种基于动态轨迹生成的几何方法.     

用圆锥曲线求解几何约束问题

几何约束求解广泛应用于机械设计,化学分子形成,几何定理证明和勘探等诸多领域,用于求解几何约束问题主要有3种方法：数值方法,符号方法和构造法,构造法用于具有简单易行的特点,因此被大多数的参数化机械设计系统作为求解几何约束问题的基本方法,针对构造法中只采用直线和圆,即直尺和圆规,来作为基本的作图工具,引进了一种新的作图工具,圆锥曲线,并且证明了在引进圆锥曲线以后,作图的范围明显大于只用直尺和圆规作图的范围;证明了一个图形能用圆锥曲线作出的充分必要条件是这个图形可以用一个三角化的次数小于等于4的方程组来描述;由于三次和四次方程的解可以显式地表示出来,所以引进圆锥曲线作为一个新的作图工具仍然可以保持原来尺规作图的简洁性和完整性。     

Solving satisfiability with less searching

A new technique, complement searching, is given for reducing the amount of searching required to solve satisfiability (constraint satisfaction) problems. Search trees for these problems often contain subtrees that have approximately the same shape. When this occurs, knowledge that the first subtree does not have a solution can be used to reduce the searching in the second subtree. Only the part of the second subtree which is different from the first needs to be searched. The pure literal rule of the Davis-Putnam procedure is a special case of complement searching. The new technique greatly reduces the amount of searching required to solve conjunctive normal form predicates that contain almost pure literals (literals with a small number of occurrences).     

Solving satisfiability problems with preferences

Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider.     

Solving weak transduction with EM

In this paper we describe an algorithm designed for learning perceptual organization of an autonomous agent. The learning algorithm performs incremental clustering of a perceptual input under reward. The distribution of the input samples is modeled by a Gaussian mixture density, which serves as a state space for the policy learning algorithm. The agent learns to select actions in response to the presented stimuli simultaneously with estimating the parameters of the input mixture density. The feedback from the environment is given to the agent in the form of a scalar value, or a reward, which represents the utility of a particular clustering configuration for the action selection. The setting of the learning task makes it impossible to use supervised or partially supervised techniques to estimate the parameters of the input density. The paper introduces the notion of weak transduction and shows a solution to it using an EM-based framework.     

Einstein谜的SAT求解

Einstein谜,亦称Zebra谜,是爱因斯坦在20世纪初提出的,他说世界上有98%的人答不出来。该问题是一个典型的逻辑推理题,可以通过SAT求解给出问题的答案。现将Einstein谜转换成SAT求解问题,并使用当前流行的SAT求解器,如MinSat,对Einstein谜进行自动求解。     

Towards a Declarative Semantics of Inheritance with Exceptions

This paper presents a declarative semantics of compositional inheritance in an object-oriented logic programming framework with explicit exceptions,based on the iterated least fixpoint semantics to normal logic programs.Taking logic objects with exceptions as a kind of non-monotonic theory,the nonmonotonicity of inheritance is achieved,which is of importance for modeling incoplete knowledge and requirement specifications in both artificial intelligence and software engineering.     

用C语言实现继承的研究

C语言在嵌入式软件开发中被广泛使用,但由于开发人员和应用场景等原因,面向对象、设计模式等优秀的软件开发方法始终没有很好地运用起来。时至今日,物联网等应用的兴起给嵌入式软件开发带来新的挑战,而传统的面向过程开发方法已经难以支撑这些复杂的应用。因此,有必要在嵌入式软件开发中引入面向对象、设计模式等优秀的软件开发方法。面向对象是现在软件方法的根基,继承是面向对象的三大特性之一,本文结合C语言的特性,对使用C语言实现继承进行了讨论。     

Linear objects: Logical processes with built-in inheritance

We present a new framework for amalgamating two successful programming paradigms: logic programming and object-oriented programming. From the former, we keep the delarative reading of programs. From the latter, we select two crucial notions: (i) the ability for objects to dynamically change their internal state during the computation; (ii) the structured representation of knowledge, generally obtained via inheritance graphs among classes of objects. We start with the approach, introduced in concurrent logic programming languages, which identifies objects with proof processes and object states with arguments occurring in the goal of a given process. This provides a clean, side-effect free account of the dynamic behavior of objects in terms of the search tree—the only dynamic entity in logic programming languages. We integrate this view of objects with an extension of logic programming, which we call Linear Objects, based on the possibility of having multiple literals in the head of a program clause. This contains within itself the basis for a flexible form of inheritance, and maintains the constructive property of Prolog of returning definite answer substitutions as output of the proof of non-ground goals. The theoretical background for Linear Objects is Linear Logic, a logic recently introduced to provide a theoretical basis for the study of concurrency. We also show that Linear Objects can be considered as constructive restriction of full Classical Logic. We illustrate the expressive power of Linear Objects compared to Prolog by several examples from the object-oriented domain, but we also show that it can be used to provide elegant solutions for problems arising in the standard style of logic programming.     

一种自适应的人工免疫异常检测算法

提出了一种基于免疫的自适应异常检测算法SAIM,该算法通过对训练抗原的学习,形成最优的抗体对记忆细胞集进行进化和更新,通过记忆细胞集采用KNN方法投票进行异常检测。实验采用著名UCI机器学习数据库的Hepatitis标准数据集,获得的分类准确率为93.5%,与现有同类算法进行比较,SAIM所取得的准确率具有一定的优越性。