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.     

f-SHOIQN:一种模糊非单调描述逻辑

针对描述逻辑无法表示语义网中模糊和非单调知识的问题,在模糊描述逻辑f-SHOIQ基础上增加弱否定构造算子,提出模糊非单调的描述逻辑f-SHOIQ_N。使用弱否定标记模糊非单调原子概念,进而表示模糊非单调规则。将模糊非单调规则引入模糊描述逻辑f-SHOIQ,用来表示模糊和非单调知识。构建f-SHOIQ_N的知识库,给出该知识库中模糊非单调知识和模糊单调知识处理方式。为处理模糊非单调知识库中规则的竞争并满足描述逻辑中概念包含、相等的问题,提出f-SHOIQ_N中竞争规则的优先级判定算法。分析f-SHOIQ_N具有的性质,并给出相关证明。     

Tuili—Ⅱ里的属性谓词及其实现

Ｔｕｉｌｉ－Ⅱ是一个基于Ｔｕｉｌｉ的知识程序设计语言，属性谓词是其中的一种新的知识表示方法。属性谓词体现了逻辑推理语言Ｔｕｉｌｉ与框架／对象在一定意义下的结合。本文主要介绍属性谓词的提出和基本思想，给出属性谓词说明及其使用的语法定义、语义及解释。讨论了属性谓词的实现思想和主要算法，并给出一个由属性谓词表示分类知识的专家系统原型的例子。     

一种改进的回答集程序更新方法

回答集程序设计是一种描述性的程序设计范例,目前已经成为知识表示和推理的一个有力工具。在实际应用中,知识库中的信息可能发生动态改变,因此需要及时对知识进行更新。通过回答集程序的更新可以实现知识库的更新,并确保更新结果的一致性。J.P.Delgrande的程序修正方法可以实现回答集程序的更新,然而该方法中存在对原程序过度修正的问题。针对该问题,提出一种新的方法来更新回答集程序,该方法通过引入优先级和惯性规则的机制,实现了回答集程序的更新。实验结果表明,该方法克服了过度修正的问题,效果良好。最后,以一个实例说明了该方法的应用。     

可能性理论与不相容知识处理

Ｄ．Ｄｕｂｏｉｓ和Ｈ．Ｐｒａｄｅ提出的可能性逻辑是一种基于可能性理论的非经典逻辑，主要和于不确定证据推理。可能性逻辑不同于模糊逻辑，因为模糊逻辑处理非布尔公式，其命题中包模糊谓词，而可能性逻辑处理布尔公式，其中只包含经典命题的和谓词。本文尝试在可能性理论的框架下进行不相容知识库的维护和问题求解。这里的知识表示是基于可能性逻辑的。为此，我们提出了两种不同的方法：第一种方法在计算命题可信度时，要考虑所     

Tuili－Ⅱ里的属性谓词及其实现

Ｔｕｉｌｉ－Ⅱ是一个基于Ｔｕｉｌｉ的知识程序设计语言，属性谓词是其中的一种新的知识表示方法．属性谓词体现了逻辑推理语言Ｔｕｉｌｉ与框架／对象在一定意义下的结合．本文主要介绍属性谓词的提出背景和基本思想，给出属性谓词说明及其使用的语法定义、语义及解释，讨论属性谓词的实现思想和主要算法，并给出一个由属性谓词表示分类知识的专家系统原型的例子．     

实现家庭服务机器人中文指令解析问题研究

为了实现家庭服务机器人在无人干预的情况下自主地执行中文指令中蕴涵的服务任务,提出一种基于回答集的中文指令任务规划方法,将组块标注和回答集编程(answer set programming,ASP)应用于家庭服务机器人任务规划。首先通过组块标注对中文指令进行预处理,然后根据转换规则将关键信息转换为谓词集,并将它转写成ASP规则。此外,给出中文服务指令处理的各个环节的实验结果,并结合实例展示从谓词集到机器人可以执行的动作序列的映射过程。最后,通过合并部分原子动作的方式对回答集进行改进,提高了求解效率,并在任务规划时加入了成本规划,确认求得最优动作序列,该方法对促进自然人-机器人交互技术的发展有重要的意义。     

基于特征增强的开放域知识库问答系统

实体消歧和谓词匹配是中文知识库问答系统（CKBQA）中的两个核心任务。针对开放域知识库中实体和谓词数量巨大,且中文问句与知识库知识在表现形式上存在差异的问题,提出一种基于特征增强的BERT的流水线式问答系统（BERT-CKBQA）,改进了上述两个子任务。采用BERT-CRF模型识别问句中提及的实体,得到候选实体集合。将问题和拼接谓词特征的候选实体输入BERT-CNN模型进行实体消歧。根据实体生成候选谓词集合,提出通过注意力机制引入答案实体谓词特征的BERT-BiLSTM-CNN模型进行谓词匹配。结合实体和谓词的得分确定查询路径来检索最终答案。该方法设计了一个中文简单问题的开放域知识库问答系统,引入预训练模型与谓词特征增强子任务特征以提升其性能,并在NLPCC-ICCPOL-2016KBQA 数据集上取得了88.75%的平均F1值,提高了系统的回答准确率。     

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.     

PREPARE: a tool for knowledge base verification

The knowledge base is the most important component in a knowledge-based system. Because a knowledge base is often built in an incremental, piecemeal fashion, potential errors may be inadvertently brought into it. One of the critical issues in developing reliable knowledge-based systems is how to verify the correctness of a knowledge base. The paper describes an automated tool called PREPARE for detecting potential errors in a knowledge base. PREPARE is based on modeling a knowledge base by using a predicate/transition net representation. Inconsistent, redundant, subsumed, circular, and incomplete rules in a knowledge base are then defined as patterns of the predicate/transition net model, and are detected through a syntactic pattern recognition method. The research results to date have indicated that: the methodology ran be adopted in knowledge-based systems where logic is used as knowledge representation formalism; the tool can be invoked at any stage of the system's development, even without a fully functioning inference engine; the predicate/transition net model of knowledge bases is easy to implement and provides a clear and understandable display of the knowledge to be used by the system     

Reasoning in inconsistent knowledge bases

Databases and knowledge bases could be inconsistent in many ways. For example, during the construction of an expert system, we may consult many different experts. Each expert may provide us with a group of rules and facts which are self-consistent. However, when we coalesce the facts and rules provided by these different experts, inconsistency may arise. Alternatively, knowledge bases may be inconsistent due to the presence of some erroneous information. Thus, a framework for reasoning about knowledge bases that contain inconsistent information is necessary. However, existing frameworks for reasoning with inconsistency do not support reasoning by cases and reasoning with the law of excluded middle (“everything is either true or false”). In this paper, we show how reasoning with cases, and reasoning with the law of excluded middle may be captured. We develop a declarative and operational semantics for knowledge bases that are possibly inconsistent. We compare and contrast our work with work on explicit and non-monotonic modes of negation in logic programs and suggest under what circumstances one framework may be preferred over another     

一阶模糊谓词逻辑公式的有限解释真度和可数解释真度的理论及其应用

通过引进公式变元集赋值的新概念给出了一阶模糊谓词逻辑（或一阶模糊语言）公式的有限解释真度及可数解释真度的定义,并讨论了它们的一系列性质及其在近似推理中的应用,从而为一阶谓词逻辑的近似推理理论提供了一种带度量的框架.     

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.     

高阶逻辑下知识表示与聚类方法的研究*

针对一阶逻辑在复杂结构数据环境中存在模式搜索空间庞大和不能发明新谓词的缺点,提出了使用类型化的高阶逻辑知识表示语言Escher去表示各种复杂结构的数据,利用其强类型语法有效地约束知识发现过程中模式的搜索空间和高阶的特点去解决新谓词构造的问题。设计了以Escher为基础的复杂结构数据中的知识发现过程和基于复杂结构数据的聚类算法,并以实验验证了其有效性。     

Man-machine communication in Portuguese: A friendly library service system

This paper enhances some of the main aspects of a system which converses in Portuguese to provide a library service covering the field of Artificial Intelligence. Its central feature is the use of logic as a single uniform language for knowledge and data representation, deductive information retrieval and linguistic analysis. The objective of designing this system was the development of a feasible method for consulting and creating data bases in natural Portuguese. The system is implemented in Prolog, a programming language essentially identical in syntax and semantics to a subset of predicate calculus in clausal form.     

因素表示的信息空间与广义概率逻辑

国内外近年来所提出的广义概率逻辑对于人工智能的发展有重要意义。能否反映变换演化的实际场景，使逻辑判断能够灵活变通，这是广义概率逻辑发展的关键。为了解决这一问题，本文的目是以信息空间作为逻辑与实际场景的接口。有了这个接口，逻辑判断就能反映变幻莫测的实际场景。本文的方法是用因素空间来定义表现论域以形成新的信息空间，将谓词中的变元取为因素，在已有的逻辑系统中加上本文所提出的背景公理，所有的推理都是在一定背景之下的推理，不同的背景会推出不同的结论。结果是新的逻辑既能维系Stone表示定理的表现要求，又能变得更加灵活有效。结论能使广义概率逻辑更有效地服务于人工智能。为了配合机制主义人工智能的需要，本文还特别提出了语法-语用对接的方法和目标驱动的逆向推理设想，最后为泛逻辑的3种连续算子对进行了数学证明。     

Constructing probabilistic graphical model from predicate formulas for fusing logical and probabilistic knowledge

Expressing knowledge as expert experience and discovering knowledge implied in data are two important ways for knowledge acquisition. Consistent combination of these two kinds of knowledge has attracted much attention due to the potential applications to knowledge fusion and wide requirements of decision support. In this paper, we focus on the probabilistic modeling of expert experience represented as logical predicate formulas, aiming at the effective fusion of logical and probabilistic knowledge. Taking qualitative probabilistic network (QPN) as the underlying framework of probabilistic knowledge implied in data as well as the abstraction of general Bayesian networks (BNs), we are to construct the probabilistic graphical model for both the given predicate formulas and the ultimate result of knowledge fusion. We first propose the concept and the construction algorithm of predicate graph (PG) to describe the dependence relations among predicate formulas, and discuss PG’s probabilistic semantics correspondingly. We then prove that PG is a probability dependency model and has the same semantics with a general probabilistic graphical model. Consequently, we give the method for fusing PG and QPN. Experimental results show the effectiveness of our methods.