Knowledge Representation of Software Component Interconnection Information for Large-Scale Software Modifications

Logic can be used to precisely express human thoughts and inferences. In this paper, an approach using first-order logic for knowledge representation of software component interconnection information to facilitate the validity and integrity checking of the interconnection among software components during software development or modification is presented. Directed graphs are first used to model the structure and behavior of a large-scale software system, and a first-order theory of directed graphs (the DG theory) is established. The interconnection behavior among software components in a large-scale software system is a directed graph which is called software component interconnection graph (CIG). The behavior of the CIG is interpreted using the DG theory and translated into logic representation. The translated logic representation is a set of logic clauses and can be considered as a set of axioms. Automated reasoning techniques based on these axioms can be used to perform the validity and integrity checking of software properties in the software development or maintenance phase.     

基于模糊框架-产生式知识表示及推理研究

知识表示是专家系统求解能力及正确性的基础。针对不同知识表示方法的局限性,采用框架与产生式知识表示法结合表示专家知识。同时鉴于传统知识表示及推理方法在描述事实生产中不确定知识及经验中的缺陷问题,将模糊推理与知识表示相结合,应用模糊因子,定量细化描述模糊知识;并结合知识表示特点应用动态加权平均匹配函数及模糊推理方法,提出基于模糊框架-产生式知识表示方法及推理的研究,量化地表示知识及推理过程,为决策人员提供更加直观、准确的推理依据。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一种基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性能逻辑及模糊集合理论作为其逻辑基础，并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性逻辑及模糊集合理论作为其逻辑基础并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

基于本体的应急救援知识库构建及其应用

针对专家系统在应急救援领域应用中存在的知识表示及推理等问题,采用基于本体的知识表示方法与基于Jena的规则推理引擎,参考简单知识工程方法论与Jena规则语法建立一个高速公路应急救援本体与推理规则,实现本体知识库的推理。将该知识库应用于高速公路应急救援系统中,结果表明其具备解决实际问题的能力;有利于领域知识的共享与重用;促进了专家系统在高速公路应急救援领域的发展。     

产生式规则专家系统的原理与实现

不确定的知识表示与知识推理是专家系统研究和开发的难点。本文利用“目标驱动”方法中控制模块、规则库和事实数据库的操作原理，使用SQL Server2000和Delphi 6．0作为开发平台，通过数据格式和算法的设计构造并实现了一个产生式规则专家系统。该系统实现了不确定性知识表示和知识推理的计算机化，用户只需要为系统提供足够的已知数据，就可以获得专家水平的结论。     

基于扩展模糊描述逻辑的软件数值模型

表示复杂的数值关系是软件建模的难点:直接表示复杂的数值关系会导致极高的软件模型推理复杂性。提 出采用模糊函数近似表示复杂数值关系,然后将扩展模糊描述逻辑作为软件模型形式化基础的软件模型建模框架,该 框架包含3个核心问题:软件数值域模糊化、软件数值知识库构造和软件数值模型推理。在软件数值域模糊化方面提 出模糊化方法和保证充分性的近似原则;在软件数值知识库构造方面讨论在模糊概念的基础上生成截集概念、构造声 明和术语公理的步骤和过程;在软件数值模型推理方面研究算法设计优化和分割等算法实现机制。     

视觉知识：跨媒体智能进化的新支点

回顾跨媒体智能的发展历程,分析跨媒体智能的新趋势与现实瓶颈,展望跨媒体智能的未来前景。跨媒体智能旨在融合多来源、多模态数据,并试图利用不同媒体数据间的关系进行高层次语义理解与逻辑推理。现有跨媒体算法主要遵循了单媒体表达到多媒体融合的范式,其中特征学习与逻辑推理两个过程相对割裂,无法综合多源多层次的语义信息以获得统一特征,阻碍了推理和学习过程的相互促进和修正。这类范式缺乏显式知识积累与多级结构理解的过程,同时限制了模型可信度与鲁棒性。在这样的背景下,本文转向一种新的智能表达方式——视觉知识。以视觉知识驱动的跨媒体智能具有多层次建模和知识推理的特点,并易于进行视觉操作与重建。本文介绍了视觉知识的3个基本要素,即视觉概念、视觉关系和视觉推理,并对每个要素展开详细讨论与分析。视觉知识有助于实现数据与知识驱动的统一框架,学习可归因可溯源的结构化表达,推动跨媒体知识关联与智能推理。视觉知识具有强大的知识抽象表达能力和多重知识互补能力,为跨媒体智能进化提供了新的有力支点。     

小数据集贝叶斯网络多父节点参数的修复

具有已知结构的小数据集贝叶斯网络多父节点参数学习是一个重要而困难的研究课题,由于信息不充分,使得无法直接对多父节点参数进行有效的估计,如何修复这些参数便是问题的核心.针对问题提出了一种有效的小数据集多父节点参数修复方法,该方法首先使用Bootstrap抽样扩展小数据集,然后分别将Gibbs抽样与最大似然树和贝叶斯网络相结合,通过依次对扩展数据按一定比例的迭代修正来实现对多父节点参数的修复.实验结果表明,这种方法能够有效地使大部分多父节点参数得到修复.     

面向对象的专家系统及其在柔性制造系统调度决策中的应用

本文把面向对象的方法用于专家系统的设计。文中结合柔性制造系统调度决策系统的设计，讨论了面向对象的知识获取、面向对象的知识表示及面向对象的推理的实现。文中介绍了面向对象的专家系统的实现软件。给出了实验结果。     

“知识表示与知识推理”知识体的教学设计

知识表示与知识推理是智能信息处理的基础,在计算机科学和人工智能的众多领域有着广泛应用。知识表示与知识推理的教学,在CC2001和CS2008给出的计算机科学知识体中占据了两个知识单元的位置,但在国内尚未得到足够重视。本文首先阐述将知识表示与知识推理作为一门课程进行教学的必要性;接下来以CC2001和CS2008为参照,给出一个32课时的"知识表示与知识推理"教学知识体;最后对教学实践中遇到的主要问题进行分析,并针对这些问题提出相应解决对策。     

A framework for building knowledge-bases under uncertainty

Managing uncertainty during the knowledge engineering process from elicitation to validation and verification requires a flexible, intuitive, and semantically sound knowledge representation. This is especially important since this process is typically highly interactive with the human user to add, update, and maintain knowledge. In this paper, we present a model of knowledge representation called Bayesian Knowledge-Bases (BKBs). It unifies a ‘if-then’ style rules with probability theory. We also consider the computational efficiency of reasoning over BKBs. We can show that through careful construction of the knowledge-base, reasoning is computationally tractable and can in fact be polynomial-time. BKBs are currently fielded in the PESKI intelligent system development environment.     

A G-net model for knowledge representation and reasoning

The G-net model for G-type knowledge representation is introduced. It is capable of modeling both static semantic knowledge and dynamic control knowledge, combining them into a loosely coupled, mixed-type knowledge hierarchy. Four reasoning algorithms for the G-net model are proposed: inheritance reasoning and recognition reasoning for semantic knowledge, event-driven reasoning for dynamic knowledge, and control table reasoning for coordination and control in a mixed-type knowledge hierarchy. Based on the knowledge-table representation, the G-net model expresses the constraints and relationships among knowledge objects explicitly so that reasoning algorithms can be implemented efficiently. Applications to information systems prototyping are discussed     

基于数据库的知识表达与推理研究

本文研究了专家系统的知识表达和推理机制,提出了基于数据库的知识表达方式。这种方式使数据库具有了知识库的功能,使知识更容易表达和管理。同时,提出了满足这种表达方式的推理机制,提高了专家系统的推理效率。     

一种基于关系数据库的知识表示和推理方法

获得一种具有广泛的知识表示方法和灵活高效的推理逻辑是专家系统研究中一直追求的一个目标?将日益发展成熟的关系数据库技术引入专家系统,论述了一种基于关系数据库的知识表示方法和推理逻辑,利用关系数据库几乎不受限制的字段个数和丰富的字段类型来表示专家知识和组织推理逻辑,达到了知识表示的广泛性和逻辑推理的高效性。这一方法可广泛应用于各领域的专家系统,在中医专家系统的实例中,取得了令人满意的效果。     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

Prajna: Adding Automated Reasoning to the Visual- Analysis Process

Applications and systems can represent knowledge in various ways. Graphic displays might help a data analyst infer new information through interactive visualizations. Knowledge represented as a collection of facts can be used for automatic inference, although it might be represented or stored in various archives, such as databases or formatted files. Developers who create applications for knowledge representation frequently must contend with not only data challenges but also challenges caused by a wide variety of software toolkits, architectures, and standards for knowledge representation. To overcome these obstacles, Vision Systems & Technology, Inc. initiated the Prajna project. The result was a Java toolkit designed to provide various capabilities for visualization, knowledge representation, geographic displays, semantic reasoning, and data fusion. This article is part of a special issue on knowledge-assisted visualization.     

融合知识表示和深度强化学习的知识推理方法

知识推理是解决知识图谱中知识缺失问题的重要方法,针对大规模知识图谱中知识推理方法仍存在可解释性差、推理准确率和效率偏低的问题,提出了一种将知识表示和深度强化学习相结合的方法RLPTransE。利用知识表示学习方法,将知识图谱映射到含有三元组语义信息的向量空间中,并在该空间中建立强化学习环境。通过单步择优策略网络和多步推理策略网络的训练,使强化学习智能体在与环境交互过程中,高效挖掘推理规则进而完成推理。在公开数据集上的实验结果表明,相比于其他先进方法,该方法在大规模数据集推理任务中取得更好的表现。