Inventory control as an identification problem based on fuzzy logic

An approach is proposed to solving inventory control problems using information available on current demand and stock. The approach is based on identification of nonlinear dependences using fuzzy knowledge bases. By tuning a fuzzy model against a learning sample, model control actions can be made very close to an expert’s decision. This approach can further be developed by creating adaptive (neuro-fuzzy) inventory control models for enterprises and trading companies. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 123–133, May–June 2006.     

Representing Software Engineering Knowledge

We argue that one important role that Artificial Intelligence can play in Software Engineering is to act as a source of ideas about representing knowledge that can improve the state-of-the-art in software information management, rather than just building intelligent computer assistants. Among others, such techniques can lead to new approaches for capturing, recording, organizing, and retrieving knowledge about a software system. Moreover, this knowledge can be stored in a software knowledge base, which serves as corporate memory, facilitating the work of developers, maintainers and users alike. We pursue this central theme by focusing on requirements engineering knowledge, illustrating it with ideas originally reported in (Greenspan et al., 1982; Borgida et al., 1993; Yu, 1993) and (Chung, 1993b). The first example concerns the language RML, designed on a foundation of ideas from frame- and logic-based knowledge representation schemes, to offer a novel (at least for its time) formal requirements modeling language. The second contribution adapts solutions of the frame problem originally proposed in the context of AI planning in order to offer a better formulation of the notion of state change caused by an activity, which appears in most formal requirements modeling languages. The final contribution imports ideas from multi-agent planning systems to propose a novel ontology for capturing organizational intentions in requirements modeling. In each case we examine alterations that have been made to knowledge representation ideas in order to adapt them for Software Engineering use.     

Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning

A model is proposed for predicting the result of a football match from the previous results of both teams. This model underlies the method of identifying nonlinear dependencies by fuzzy knowledge bases. Acceptable simulation results can be obtained by tuning fuzzy rules using tournament data. The tuning procedure implies choosing the parameters of fuzzy-term membership functions and rule weights by a combination of genetic and neural optimization techniques. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 171–184, July–August 2005.     

Neurolinguistic identification of nonlinear dependences

A method is proposed for the identification of nonlinear dependences on the basis of a composition of a fuzzy knowledge base and a neural network. The structure of a neurofuzzy network that is isomorphic to a system of linguistic statements of the form “if-then” is specified, and analytical models for training are obtained. The efficiency of the proposed method of identification is illustrated by a computer experiment. Translatedfrom Kibernetika i Sistemnyi Analiz, No. 2, pp. 37–44, March–April, 2000     

On the consistency of knowledge bases: the COVADIS system

It is currently thought in the knowledge-based systems (KBS) domain that sophisticated tools are necessary for helping an expert with the difficult task of knowledge acquisition. The problem of detecting inconsistencies is especially crucial. The risk of inconsistencies increases with the size of the knowledge base; for large knowledge bases, detecting inconsistencies "by hand" or even by a superficial survey of the knowledge base is impossible. Indeed, most inconsistencies are due to the interaction between several rules via often deep deductions. In this paper, we first state the problem and define our approach in the framework of classical logic. We then describe a complete method to prove the consistency (or the inconsistency) of knowledge bases that we have implemented in the COVADIS system.     

Information Extraction and Knowledge Acquisition from Texts Using Bilingual Question–Answering

A novel approach is introduced in this paper for the implementation of a question–answering based tool for the extraction of information and knowledge from texts. This effort resulted in the computer implementation of a system answering bilingual questions directly from a text using Natural Language Processing. The system uses domain knowledge concerning categories of actions and implicit semantic relations. The present state of the art in information extraction is based on the template approach which relies on a predefined user model. The model guides the extraction of information and the instantiation of a template that is similar to a frame or set of attribute value pairs as the result of the extraction process. Our question–answering based approach aims to create flexible information extraction tools accepting natural language questions and generating answers that contain information extracted from text either directly or after applying deductive inference. Our approach also addresses the problem of implicit semantic relations occurring either in the questions or in the texts from which information is extracted. These relations are made explicit with the use of domain knowledge. Examples of application of our methods are presented in this paper concerning four domains of quite different nature. These domains are: oceanography, medical physiology, aspirin pharmacology and ancient Greek law. Questions are expressed both in Greek and English. Another important point of our method is to process text directly avoiding any kind of formal representation when inference is required for the extraction of facts not mentioned explicitly in the text. This idea of using text as knowledge base was first presented in Kontos [7] and further elaborated in [9,11,12] as the ARISTA method. This is a new method for knowledge acquisition from texts that is based on using natural language itself for knowledge representation.     

A Framework for Method-Specific Knowledge Compilation from Databases

Generality and scale are important but difficult issues in knowledge engineering. At the root of the difficulty lie two challenging issues: how to accumulate huge volumes of knowledge and how to support heterogeneous knowledge and processing. One approach to the first issue is to reuse legacy knowledge systems, integrate knowledge systems with legacy databases, and enable sharing of the databases by multiple knowledge systems. We present an architecture called HIPED for realizing this approach. HIPED converts the second issue above into a new form: how to convert data accessed from a legacy database into a form appropriate to the processing method used in a legacy knowledge system. One approach to this reformed issue is to use method-specific compilation of data into knowledge. We describe an experiment in which a legacy knowledge system called INTERACTIVE KRITIK is integrated with an ORACLE database. The experiment indicates the computational feasibility of method-specific data-to-knowledge compilation.     

A Model-Theoretic Approach for Recovering Consistent Data from Inconsistent Knowledge Bases

One of the most significant drawbacks of classical logic is its being useless in the presence of an inconsistency. Nevertheless, the classical calculus is a very convenient framework to work with. In this work we propose means for drawing conclusions from systems that are based on classical logic, although the information might be inconsistent. The idea is to detect those parts of the knowledge base that cause the inconsistency, and isolate the parts that are recoverable. We do this by temporarily switching into Ginsberg/Fitting multivalued framework of bilattices (which is a common framework for logic programming and nonmonotonic reasoning). Our method is conservative in the sense that it considers the contradictory data as useless and regards all the remaining information unaffected. The resulting logic is nonmonotonic, paraconsistent, and a plausibility logic in the sense of Lehmann.     

Compact Representation of Knowledge Bases in Inductive Logic Programming

In many applications of Inductive Logic Programming (ILP), learning occurs from a knowledge base that contains a large number of examples. Storing such a knowledge base may consume a lot of memory. Often, there is a substantial overlap of information between different examples. To reduce memory consumption, we propose a method to represent a knowledge base more compactly. We achieve this by introducing a meta-theory able to build new theories out of other (smaller) theories. In this way, the information associated with an example can be built from the information associated with one or more other examples and redundant storage of shared information is avoided. We also discuss algorithms to construct the information associated with example theories and report on a number of experiments evaluating our method in different problem domains.     

COMBINING KNOWLEDGE BASES CONSISTING OF FIRST-ORDER THEORIES

Consider the construction of an expert system by encoding the knowledge of different experts. Suppose the knowledge provided by each expert is encoded into a knowledge base. Then the process of combining the knowledge of these different experts is an important and nontrivial problem. We study this problem here when the expert systems are considered to be first-order theories. We present techniques for resolving inconsistencies in such knowledge bases. We also provide algorithms for implementing these techniques.     

Automatic refinement of knowledge bases with fuzzy rules

The knowledge-acquisition bottleneck obstructs the development of expert systems. Refinement of existing knowledge bases is a subproblem of the knowledge-acquisition problem. The paper presents a HEuristic REfinement System (HERES), which refines rules with mixed fuzzy and nonfuzzy concepts represented in a variant of the rule representation language Z-II automatically. HERES employs heuristics and analytical methods to guide its generation of plausible refinements. The functionality and effectiveness of HERES are verified through various case studies. It has been verified that HERES can successfully refine knowledge bases. The refinement methods can handle imprecise and uncertain examples and generate approximate rules. In this aspect, they are better than other famous learning algorithms such as ID3^15–18, AQ11, and INDUCE^{14, 19, 20} because HERES' methods are currently unique in processing inexact examples and creating approximate rules.     

通用地面气象仪器故障诊断知识获取方法的研究

专家系统面临的主要问题仍然是知识获取的"瓶颈"问题.本文针对地面气象仪器故障诊断的特点,介绍一种基于诊断实例的自动规则提取法,对地面气象仪器的诊断实例进行分析,提取有用的诊断知识.这种知识获取方法的引入,将有效解决通用地面气象仪器故障诊断专家系统的可诊设备的扩展问题,大大提高专家系统的知识获取的速度.     

基于知识库分割的多知识库整合方法

知识库是集团企业云制造平台中知识服务的重要基础,知识库的质量直接决定着知识服务的质量。目前单一分散的知识库不能提供统一的知识资源全局视图,不利于知识资源的共享。多知识库整合技术已经成为该领域的研究热点之一。提出一种基于知识库分割的多知识库整合方法,采用基于蚁群聚类的分割策略,将知识库有效划分为知识块集。在知识块间利用语义概念映射生成知识块间映射图,从而实现多知识库整合。通过对算法的时间复杂度进行分析,表明该方法在时间复杂度方面要优于基于最小概念集的多知识库整合方法;实验结果也表明该方法在运行性能方面明显优于已有的方法。     

利用海量知识库实现实体标注的一种方法

互联网上聚集了大量的文本、图像等非结构化信息,RDF作为W3C提出的互联网上的资源描述框架,非常适合于描述网络上的非结构化信息,因此形成了大量的RDF知识库,如Freebase、Yago、DBPedia等。RDF知识库中包含丰富的语义信息,可以对来自网页的名字实体进行标注,实现语义扩充。将网页上的名字实体映射到知识库中对应实体上称作实体标注。实体标注包括两个主要部分:实体间的映射和标注去歧义。利用海量RDF知识库的特性,提出了一种有效的实体标注方法。该方法采用简单的图加权及计算解决实体标注的去歧义问题。该方法已在云平台上实现,并通过实验验证了其准确度和可扩展性。     

A split-combination approach to merging knowledge bases in possibilistic logic

We propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: the splitting step and the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, different classes of subbases are combined using different operators. Our merging approach is applied to knowledge bases which are self-consistent and results in a knowledge base which is also consistent. Two operators are proposed based on two different splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the t-conorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntax-based merging operators in classical logic. This paper is a revised and extended version of [36].     

基于Web的种子驱动的“isa”关系的模式表示和获取

"isa"关系是知识获取中一种基本的语义关系,给出了中文"isa"关系的模式表示和获取方法。提出基于模式特征的种子选取算法和基于特征词的种子驱动的模式学习算法。实验说明了方法的有效性。     

融合PFS与RS的案例知识供需匹配研究

面对日益丰富的知识资源,对知识供需匹配问题的研究有助于知识资源的有效配置、解决用户“知识迷向”问题,实现知识资源价值最大化,具有重要的理论意义和实践价值。将毕达哥拉斯模糊集（PFS）与模糊粗糙集相结合,建立毕达哥拉斯模糊粗糙集（PFRS）模型,用以处理模糊不确定性知识资源的知识供需匹配问题;通过引入PFS相关测度对属性相似度进行改进,并基于知识属性权重求出用户需求与案例知识之间的视图相似度,确定匹配结果。实证分析验证了所提方法的合理性与可行性,与既有方法相比,通过引入毕达哥拉斯模糊集,对模糊知识的描述更加客观,具备一定的理论优势;同时,用加权相关系数表示视图相似度,使匹配结果更加准确,提高了匹配精度。     

关系数据库表示规则知识的理论与方法

知识库及其组织结构在智能系统中具有相当重要的作用。针对当前知识库的难点问题,结合实际应用,给出了一种基于关系型数据库的简便通用的知识库结构设计,利用表记录项中的复合数据表示复杂信息,同时,提出了一种编码方法来保证知识的管理与检索。最后给出了一个实例,表明该方法可以完善地表达事实——规则知识体系,具有很好的普适性。     

A framework of knowledge versioning management

Abstract: Knowledge is an inherently dynamic entity continuously changing and evolving. In many cases, the coexistence of different versions of the same core knowledge is a necessity. So is the availability of the proper environment and tools to deal with knowledge versioning. In this paper, a framework of knowledge versioning management is proposed and implemented dealing with hybrid knowledge representation models using frames and rules. This framework facilitates knowledge version handling and maintenance, improving, in parallel, knowledge sharing and reuse. Knowledge components are stored in a set of tables and handled as data under the auspices of a database management system. The proper structure of tables and their relationships allows the creation of independent knowledge modules. Several knowledge modules can be assembled to construct higher level modules, which finally form versions of knowledge. Corresponding knowledge base versions consist of several knowledge modules easy to handle and process in various application areas. The proposed framework has been implemented and thoroughly examined in an application area of great importance, such as pest management.     

Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts

Theknowledge transfer problem in artificial intelligence consists of finding effective ways to elicit information from a human expert and represent it in a form suitable for use by an expert system. One approach to formalizing and guiding this knowledge transfer process for certain types of expert systems is to use psychometric scaling methods to analyze data on how the human expert compares or groups solutions. For example, Butler and Corter [1] obtained judgments of thesubstitutability of solutions from an expert, then analyzed the resulting data via techniques for fitting trees and extended trees [2]. The expert's interpretation of certain aspects of the solutions were directly encoded as production rules, allowing rapid prototyping. In this paper we consider the problem of combining information from multiple experts. We propose the use of three-way or individual differences multidimensional scaling, tree-fitting, and unfolding models to analyze two types of data obtainable from the multiple experts: judgments of the substitutability of pairs of solutions, and judgments of the appropriateness of specific solutions to specific problems. An application is described in which substitutability data were obtained from three experts and analyzed using the SINDSCAL program [3] for three-way multidimensional scaling [4].