基于高阶逻辑的复杂结构数据半监督聚类

半监督聚类近年来成为了机器学习和数据挖掘领域的研究热点.目前存在的半监督聚类方法都采用属性-值的知识表示方式.但属性-值语言在表示复杂结构数据时存在很多弊端,而基于高阶逻辑的知识表示语言Escher能较好地表示复杂结构数据.在Fscher的知识表示方式下,首先当先验知识是实例之间的约束信息时,提出了搜索K-Means算法的K个初始质心的方法;其次,时先验知识不完全、能够发现的初始质心的个数,r小于K的情况,提出了搜索其余的K-r个初始质心的算法MSS-KMeans和SMSS-KMeans;最后在复杂结构数据集上,验证了所提算法的可行性.最终的实验结果表明,基于高阶逻辑知识表示方式的丰监督聚类方法要优于基于属性-值语言的半监督聚类方法.     

基于中介逻辑的模糊知识推理的搜索处理

中介逻辑是一种区分矛盾否定与对立否定、肯定一些对立知识间存在中介对象的逻辑系统。基于中介谓词逻辑描述模糊知识,合理修改与或图,将每一谓词表达式视为状态结点,把逻辑规则集合表示为状态搜索空间。在传统与或图搜索算法的基础上,修改启发函数,将模糊知识的推理问题转化为状态空间中的搜索问题,并给出了一种否定信息的处理方法。     

谓词逻辑在关系数据库中的应用

本文旨在研究将谓词逻辑及公理化理论应用于关系数据库中表示数据子语言,应用谓词逻辑作为它的数学基础,使得对这些语言的研究成为对谓词逻辑的研究,优化数据子语言的表示成为对谓词逻辑的化简问题。     

谓词逻辑在关系数据库中的应用

本文旨在研究将谓词逻辑及公理化理论应用于关系数据库中表示数据子语言,应用谓词逻辑作为它的数学基础,使得对这些语言的研究成为对谓词逻辑的研究,优化数据子语言的表示成为对谓词逻辑的化简问题.     

从经典逻辑知识构建ASP知识库的新方法

回答集程序设计(ASP)是一种主流的非单调知识表示工具。为了能够在利用ASP求解问题过程中使用现有的以经典逻辑表示的知识,给出了一种把以谓词逻辑公式表示的约束型知识和定义型知识转化为ASP程序或知识库的新方法,并以实例说明了其有效性。该方法满足转化后ASP程序的回答集与原公式集的模型具有一一对应关系。在实际应用中,该方法提供了一项从现存的以谓词逻辑为表示语言的知识库,构建以ASP为知识表示语言的非单调知识库的技术。     

基于高阶逻辑的复杂结构归纳学习研究

归纳学习的目的在于发现样例与离散的类之间的映射关系,样例及归纳的映射都需用某个形式化语言描述.归纳学习器采用的形式化语言经历了属性-值语言、一阶逻辑、类型化的高阶逻辑三个阶段,后者能克服前二者在知识表达及学习过程中的很多缺点.本文首先阐述了基于高阶逻辑的复杂结构归纳学习产生的历史背景;其次介绍了基于高阶逻辑的编程语言--Escher的知识描述形式及目前已提出的三种学习方法;复杂结构的归纳学习在机器学习领域的应用及如何解决一些现实问题的讨论随后给出; 最后分析了复杂结构归纳学习的研究所面临的挑战性问题.     

并发系统中谓词行为图的行为时序逻辑表达

行为时序逻辑(TLA)组合时序逻辑与行为逻辑, 可以对并发系统进行描述与验证, 它引入动作和行为的概念, 使得系统和属性可用它的规约公式表示, 但存在用TLA描述复杂系统时TLA公式复杂且难以理解的不足。类似于状态转移图, 对于并发转移可以用谓词行为图进行图形化表示, 谓词行为图与行为时序逻辑规约具有相同的表达能力。介绍行为时序逻辑的语法、语义及简单推理规则, 用一个简单的实例说明使用谓词行为图去描述并发转移系统的有效性, 并用系统规约的TLA公式对谓词行为图表达能力进行证明, 表明两者具有等价性, 为描述和分析并发转换系统提供了一种可行的方法。     

自然语言处理中的逻辑文法

逻辑文法是指用谓词逻辑来表达的文法。它属于计算语言学的范畴,是逻辑程序设计和现代语言学相结合的产物。在人工智能的自然语言处理等领域里,谓词逻辑通常用来描述知识和逻辑推理。70年代,逻辑用于程序设计的思想以Prolog语言的形式投入应用以来,谓词逻辑不再仅仅用于描述这些问题,还作为逻辑程序设计的工具去描述解决问题的过程。PROLOG语言使得逻辑和程序设计这     

基于位串编码的遗传归纳逻辑程序设计

归纳逻辑程序设计是基于一阶逻辑的数据挖掘新方法。一阶规则挖掘是目标谓词和背景知识谓词对应的各种原子的复杂组合优化问题。该文根据Occam’s razor原理提出原子的位串编码,设计相应的遗传箅子,基于sequential covering策略提出采用遗传算法作为搜索策略的遗传归纳逻辑程序设计算法GILP。在连通图问题和gcd问题上验证算法的可行性。     

基于描述逻辑的面向管理决策的异构知识的表示

构筑面向管理决策的智能系统中常存在知识库结构的复杂度和系统效率的平衡问题，为此用描述性逻辑表示面向管理决策的3类4种异构知识．由于描述性逻辑结合了谓词逻辑和面向对泉的知识表示，通过基于描述性逻辑的异构知识的统一表示，降低广义知识库的结构复杂性，并基于描述性逻辑的知识表示系统具有有效的推理能力，保证一定的系统效率．这项研究对于对面向管理决策智能系统的知识库构筑具有一定的理论借鉴作用和实际应用价值．     

Learning concept descriptions with typed evolutionary programming

Examples and concepts in traditional concept learning tasks are represented with the attribute-value language. While enabling efficient implementations, we argue that such propositional representation is inadequate when data is rich in structure. This paper describes STEPS, a strongly-typed evolutionary programming system designed to induce concepts from structured data. STEPS higher-order logic representation language enhances expressiveness, while the use of evolutionary computation dampens the effects of the corresponding explosion of the search space. Results on the PTE2 challenge, a major real-world knowledge discovery application from the molecular biology domain, demonstrate promise.     

Lp, a logic for representing and reasoning with statistical knowledge

This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an important component of our world knowledge and that such knowledge is used in many different reasoning tasks. The work is further motivated by the observation that previous formalisms for representing probabilistic information are inadequate for representing statistical knowledge. The representation mechanism takes the form of a logic that is capable of representing a wide variety of statistical knowledge, and that possesses an intuitive formal semantics based on the simple notions of sets of objects and probabilities defined over those sets. Furthermore, a proof theory is developed and is shown to be sound and complete. The formalism offers a perspicuous and powerful representational tool for statistical knowledge, and a proof theory which provides a formal specification for a wide class of deductive inferences. The specification provided by the proof theory subsumes most probabilistic inference procedures previously developed in AI. The formalism also subsumes ordinary first-order logic, offering a smooth integration of logical and statistical knowledge.     

A decidable first-order logic for knowledge representation

Decidable first-order logics with reasonable model-theoretic semantics have several benefits for knowledge representation. These logics have the expressive power of standard first order logic along with an inference algorithm that will always terminate, both important considerations for knowledge representation. Knowledge representation systems that include a faithful implementation of one of these logics can also use its model-theoretic semantics to provide meanings for the data they store. One such logic, a variant of a simple type of first-order relevance logic, is developed and its properties described. This logic, although extremely weak, does capture a non-trivial and well-motivated set of inferences that can be entrusted to a knowledge representation system.This is a revised and much extended version of a paper of the same name that appears in the Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, California, 1985.     

复杂结构归纳学习研究

复杂结构归纳学习的需求近年来快速增长。复杂结构归纳学习方法按照知识表示方式不同分为基于逻辑的方法与基于数学图的方法。阐述了复杂结构归纳学习研究的历史沿革,介绍、分析和对比了不同知识表示方式下的学习方法,给出了复杂结构归纳学习将来发展面临的挑战和需重点解决的问题。     

A Polynomial Approach to the Constructive Induction of Structural Knowledge

The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation, term subsumption formalisms have been developed which are efficient and expressive. In this article, a learning algorithm, KLUSTER, is described that represents concept definitions in this formalism. KLUSTER enhances the representation language if this is necessary for the discrimination of concepts. Hence, KLUSTER is a constructive induction program. KLUSTER builds the most specific generalization and a most general discrimination in polynomial time. It embeds these concept learning problems into the overall task of learning a hierarchy of concepts.     

Formalization of object-oriented database model with rules

The paper investigates knowledge representation in an object-oriented database management system first within the data model with rules and second in the computational model by using logic. Issues of structure, integrity, and retrieval are focused on. The proposed system provides object-oriented concepts for describing complex structured data, rules for expressing object-dependent constraints and object associations, and, finally, logic for inference and retrieval.     

STRUCTURED REPRESENTATION OF FMS INTEGRATING SI-NETS AND HIGH LEVEL PETRI NETS

In this paper a structured knowledge-based approach to the representation and scheduling of flexible manufactoring systems (FMSs) is described. Our approach is based on a structured conceptual representation (a KL-ONE-like Si-net representation formalism), extended with an instant-based temporal reasoning formalism. Furthermore, the approach integrates a particular extension to high-level Petri nets (PNs), structured timed colored Petri nets (STCPNs), for the modeling and simulation of the FMS. Such a representation scheme allows us to use SI-nets' good properties related to inference (classification and inheritance), which are lacking in PNs, and at the same time provides an extension toward an explicit representation for time. The integration of Si-nets with PNs is necessary because simulation and low-level coordination of FMSs require a procedural approach that is not within the aims of Si-nets. Therefore, procedural and symbolic levels, corresponding to the different hierarchical levels of the representation and control system of the FMS, coexist in the system. Using a qualitative terminology, we may also call them analog and symbolic knowledge. We assume that such a hybrid representation system may be useful, since a procedural representation, integrated within a logic formalism, can increase the expressive power without complicating the notation or the representation itself. The paper describes both the representational aspects and the modeling of the control system of the FMS, focusing on the interaction mechanisms among the different levels of representation. In particular, we show how an STCPN-based model can be automatically derived starting from the symbolic component of the system. A particular FMS case study, regarding a class of problems of resource-constrained multiproject scheduling (where projects are sets of tasks temporally related), is discussed.     

国家知识基础设施中的数学知识表示

数学知识表示是知识表示中的一个重要方面,是数学知识检索、自动定理机器证明、智能教学系统等的基础.根据在设计NKI(national knowledge infrastructure)的数学知识表示语言中遇到的问题,并在讨论了数学对象的本体论假设的基础上提出了两种数学知识的表示方法:一种是以一个逻辑语言上的公式为属性值域的描述逻辑;另一种是以描述逻辑描述的本体为逻辑语言的一部分的一阶逻辑.在前者的表示中,如果对公式不作任何限制,那么得到的知识库中的推理不是可算法化的;在后者的表示中,以描述逻辑描述的本体中的推理是可算法化的,而以本体为逻辑语言的一部分的一阶逻辑所表示的数学知识中的推理一般是不可算法化的.因此,在表示数学知识时,需要区分概念性的知识(本体中的知识)和非概念性的知识(用本体作为语言表示的知识).框架或者描述逻辑可以表示和有效地推理概念性知识,但如果将非概念性知识加入到框架或知识库中,就可能使得原来可以有效推理的框架所表示的知识库不存在有效的推理算法,甚至不存在推理算法.为此,建议在表示数学知识时,用框架或描述逻辑来表示概念性知识;然后,用这样表示的知识库作为逻辑语言的一部分,以表示非概念性知识.