USING META-PROGRAMMING,CONSTRAINT LOGIC PROGRAMMING AND APPROXIMATE REASONING FOR EXPERT SYSTEMS DESIGN

Abstract

This paper presents an enhancement of the CARESS system—A Constraint Approximative Reasoning System Support—introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives:

(1)knowledge representation and manipulation techniques and to implement them in PROLOG III, and

(2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems

It has already been proven that meta-programming and logic programming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. Constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.     

Model-based remotely-sensed imagery interpretation

Abstract

We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.     

Confidence measures for predictions in fuzzy inductive reasoning

This paper deals with the assessment of the reliability of predictions made in the context of the fuzzy inductive reasoning methodology. The reliability of predictions is assessed by means of two separate confidence measures, a proximity measure and a similarity measure. A time series and a single-input/single-output system are used as two different applications to study the viability of these confidence measures.     

Predicting financial activity with evolutionary fuzzy case-based reasoning

Predicting financial activity through examining the short-term liquidity is crucial within today’s turbulent financial environment. Firms, governments, and individuals all need an effective methodology based on liquidity information that plays performance deterioration warning a priori bankruptcy prediction. In this paper, we propose a hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate. GAs are used to determine the optimal or near-optimal weight vector of financial features expressed in linguistic values by the expert. A fuzzy k-NN-based CBR scheme is designed to compute memberships of financial activity rates and to provide a more flexible and practical mechanism for acquiring, creating, and reusing the expert’s decision knowledge. An empirical experimentation using 746 publicly traded Taiwanese firms shows that the average accuracy of the rating is about 92.36%, which is superior to other related models. The proposed approach not only can lend support to the decision of an expert, but also allow proper feedback for the expert to improve the quality of the decision.     

Designing intelligent litigation support tools: The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.     

某武器系统电控设备的故障诊断专家系统

以Visual Basic6.0为开发环境，Access97为数据库结构形式，DAO为数据库访问技术，开发了某武器系统电控设备故障诊断专家系统，介绍了系统的功能组成和实现方法。研究了一般产生式规则与模糊产生式规则相结合的知识表示方法以及精确推理与模糊推理相结合、基于规则的推理和基于实例的推理相结合的推理机制。     

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。     

COMBINED QUALITATIVE/QUANTITATIVE SIMULATION MODELS OF CONTINUOUS-TIME PROCESSES USING FUZZY INDUCTIVE REASONING TECHNIQUES

Abstract

A new mixed quantitative and qualitative simulation methodology based on fuzzy inductive reasoning is presented. The feasibility of this methodology is demonstrated by means of a simple hydraulic control system. The mechanical and electrical parts of the control system are modeled using differential equations, whereas the hydraulic part is modeled using fuzzy inductive reasoning. The technique is described in detail in the first part of this paper. The example is shown in the second part of the paper. The mixed quantitative and qualitative model is simulated in ACSL, and the simulation results are compared with those obtained from a fully quantitative model. The example was chosen as a simple to describe, yet numerically demanding process whose sole purpose is to prove the concept. Several practical applications of this mixed modeling technique are mentioned in the paper. but their realization has not yet been completed     

FMS智能故障诊断专家系统的建立和开发

采用基于人工智能的故障诊断专家系统方法，附以模糊数学、神经网络、机器学习、数据库等理论，解决故障诊断中知识的合理表达，基于符号和数值的多种快速推理机制，知识的自动获取及知识库智能化管理等关键技术，建立了一个智能模糊故障诊断专家系统。     

STRATEGIC KNOWLEDGE IN AN EXPERT SYSTEM FOR AGRICULTURE

Abstract

Most of the expert systems applied in vegetal pathology treat the problem of selecting treatment in a conventional manner, by means of production rules that associate to each pathology the most adequate chemical product. This makes it difficult to generate useful explanations. In order to generate satisfactory explanations the knowledge of the system must be based on the strategies used by human experts. This article introduces our approach for the identification and representation of strategic knowledge in an expert system for plague control in greenhouses. We present an introduction to the application domain and make an analysis of the strategic knowledge implied. We distinguish between the underlying strategy and the practical strategy used by the expert for the solution of the problem. From this we propose a preliminary architecture based on strategic reasoning agents.     

Data-driven fuzzy rule generation and its application for student academic performance evaluation

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data. Khairul Rasmani is a lecturer at the Faculty of Information Technology and Quantitative Sciences, Universiti Teknologi MARA, Malaysia. He received his Masters Degree in Mathematical Education from University of Leeds, UK in 1997 and his Ph.D. degree from University of Wales, Aberystwyth, UK in December 2005. His research interests include fuzzy approximate reasoning, fuzzy rule-based systems and fuzzy classification systems. Qiang Shen is a Professor and the Director of Research with the Department of Computer Science at the University of Wales, Aberystwyth, UK. He is also an Honorary Fellow at the University of Edinburgh, UK. His research interests include fuzzy systems, knowledge modelling, qualitative reasoning, and pattern recognition. Prof. Shen serves as an associate editor or editorial board member of a number of world leading journals, including the IEEE Transactions on Systems, Man, and Cybernetics (Part B), the IEEE Transactions on Fuzzy Systems, and Fuzzy Sets and Systems. He has acted as a Chair or Co-chair at a good number of major conferences in the field of Computational Intelligence. He has published a book and over 170 peer-refereed articles in international journals and conferences in Artificial Intelligence and related areas.     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

冲裁工艺设计决策专家系统及实现

冲裁工艺设计是一种设计决策问题,有一定代表性,很需要采用专家系统技术求解。讨论冲裁工艺设计专家系统的实现技术,包括工艺方案选择和工步设计等问题的解决方案、面向对象的知识表示和推理机制、模糊知识表示与模糊推理技术的灵活应用、图形的自动识别处理与生成等。     

The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network

The single-input single-output or multi-input single-output polygonal fuzzy neural network can accomplish some information disposing based on a finite number of points of polygonal fuzzy number. Although it does not depend on a precise mathematical model, it involves logical reasoning, numerical calculation and nonlinear functional approximation. The multi-input multi-output (MIMO) polygonal fuzzy neural network model is proposed for the first time in this article. The two different algorithms are designed in the input layer and hidden layer, respectively, and some parameters of the connection weights in the isolation layered manner can be optimized. Particularly, the neurons in the hidden layer are optimized one by one. Results showed that the isolation layered optimization algorithm of MIMO polygonal fuzzy neural network could improve the computational efficiency and convergent rate.

     

基于对象模型的自组织专家系统

在研制的一个基于对象模型的自组织专家系统中 ,通过对机器人的行走装置进行模型化 ,建立了对象的模糊知识库 ,并根据控制的目标 ,设计了推理机。系统无需精确的数学模型 ,能根据输入、输出变量 ,自动修改控制规则 ,达到优化控制的目的。     

Expert systems in ergonomics: requirements and an approach

Abstract

Industry currently fails to derive maximum benefit from ergonomics. This is mainly due to an imbalance between the large amount of ergonomie knowledge available and the limited amount actually applied. The use of the methodology of expert systems in ergonomics is one possibility for reducing this imbalance. This paper presents basic requirements for the development of expert systems. The particular requirements for the application of expert systems in ergonomics are also derived. Finally, the stage of development of an expert system for assessing lifting and carrying tasks is presented, and practical examples for the application of the system are discussed.     

船用计程仪故障诊断模糊专家系统

将模糊数学、模糊诊断原理及自动检测技术相结合,提出基于模糊推理的船用计程仪故障诊断专家系统。结合船用计程仪的特点,设计硬件平台并采用模糊产生式知识表示法,将模糊关系矩阵和语义距离相结合,设计并实现了船用计程仪故障诊断模糊专家系统的各个功能模块。实验结果表明,该系统可对船用计程仪实现高效、可靠的故障诊断。     

基于数据库的电力设备故障诊断模糊专家系统的设计与实现

采用SQL Anywhere 5.0设计知识库。PowerBuilder6.5编程实现了电力设备故障诊断模糊专家系统,其知识的表示采用了模糊产生式表示式,引进了模糊匹配与加权模糊逻辑进行模糊推理,实现了一种较为理想的非精确推理。