纺织专家系统中知识与知识库的应用

专家系统是人工智能的一个分支，是一种模拟专家决策能力的计算机系统，知识库是系统的重要组成部分，是系统的核心。本文结合纺织工艺设计及管理专家系统介绍了基于知识的专家系统的概念和结构，对系统中知识的获取、存储方式予以说明．并对其知识的表示方法加以阐述。     

Constructing and consulting the knowledge base of an expert systems shell

Abstract: This paper presents an expert system shell whose inference mechanism uses backward chaining. In particular the modules devoted to constructing and consulting the knowledge base are illustrated. The programming environment is based on the Arity-Prolog language, a popular Prolog dialect running on IBM PCs and compatibles.     

基于模糊理论的造纸专家系统--知识库的设计

基于模糊理论的造纸专家系统(PMES)的研究对于保障设备的稳定运行,提高产品质量具有重要的意义.本文探讨了模糊理论在知识表示方面如何和专家系统进行有效地结合,并对PMES的知识库进行设计.对知识库采用知识分级的方式,使知识的表达更具有层次性.通过各个知识表中的相互约束关系保证知识的一致性.     

Development of an integrated expert system for fluvial hydrodynamics

During recent decades, rapid developments in computer science and industry have been influencing every aspect of our social life. Computer Aided Design (CAD) techniques have a significant impact in engineering fields. For fluvial engineering fields, a large number of microcomputer-based programs are applied for hydrodynamics analyses during the project design stage. Recently, achievements in Artificial Intelligence (AI) have been applied in many fields. The application of expert system techniques is an attractive trend in modern design practice. It is believed that the synthetic utilization of CAD and AI techniques would become more attractive for engineers. Yet the studies on this topic are scarce. This paper describes the architecture and implementation of an integrated expert system that assists hydraulic engineers to solve the unsteady open channel flow in river networks. A methodology of combining numerical analysis software into an expert system is presented. The verification, validation, application, and future development of this system are also presented.     

Development of an expert system advisor for anaesthetic control.

A review is given of numerous approaches which have been taken to provide automated control of depth of anaesthesia. Most of these approaches use a single indicator for anaesthetic state, and do not perform adequately for such a complex system. It is desirable, therefore, to merge a number of qualitative clinical signs and quantitative on-line measurements to provide decision-support for control of anaesthetic depth. The paper describes such an expert system called Resac (Real time Expert System for Advice and Control). Details of the knowledge representation and inference structure are given, together with the method adopted for propagating uncertainty measures, combining fuzzy information and merging quantitative and qualitative indicators. The importance of good human machine interface design is shown via the GEM-based graphics of Resac.     

A knowledge sorting process for a product design expert system

Knowledge sorting is one way to organize the knowledge acquired from the domain expert(s) and various sources. Well-organized knowledge representation will make rule extraction much easier. This paper describes a knowledge sorting process that was developed to facilitate the rule extraction for a product design expert system. The process capitalizes on the relationships between design attributes and factors, dependent and independent variables, and consists of three stages: identification of knowledge sources, generation of taxonomic trees, and organization of acquired knowledge. An example applies the sorting process to the development of an expert system for the design of wood head golf clubs.     

搅拌设备化工预设计专家系统的知识处理

详细论述了搅拌设备化工预设计专家系统的知识处理方法。系统采用黑板结构将多种知识源组合在一起来实现问题的求解,即采用基于规则的推理、基于模糊逻辑的模式匹配和传统的数据库查询等技术,来分别处理搅拌设备设计过程中的人类专家经验、设计公式和机械部件标准等不同种类的知识。此专家系统的结构和知识处理的方法具有较强的灵活性,非常适用于复杂的工业过程设计领域。     

搅拌设备化工预设计专家系统的知识处理

详细论述了搅拌设备化工预设计专家系统的知识处理方法。系统采用黑板结构将多种知识源组合在一起来实现问题的求解,即采和基于规则的推理、基于模糊逻辑的模式匹配和传统的数据库查询等技术,来分别处理搅拌设备设计过程中的人类专家经验、设计公式和机械部件标准等不同种类的知识。此专家系统的结构的知识处理的方法具有较强的灵活性,非常适用于复杂的工业过程设计领域。     

Prototypical knowledge for expert systems

Knowledge of situations typically encountered in performing a task is an important and useful source of information for solving that task. This paper presents a system that uses a representation of prototypical knowledge to guide computer consultations, and to focus the application of production rules used to represent inferential knowledge in the domain. The explicit representation of control knowledge for each prototypical situation is also emphasized.     

Multi-criteria scheduling: an agent-based approach for expert knowledge integration

In this work, we present an agent-based approach to multi-criteria combinatorial optimization. It allows to flexibly combine elementary heuristics that may be optimal for corresponding single-criterion problems. We optimize an instance of the scheduling problem 1|d _j |∑C _j ,L _max and show that the modular building block architecture of our optimization model and the distribution of acting entities enables the easy integration of problem specific expert knowledge. We present a universal mutation operator for combinatorial problem encodings that allows to construct certain solution strategies, such as advantageous sorting or known optimal sequencing procedures. In this way, it becomes possible to derive more complex heuristics from atomic local heuristics that are known to solve fractions of the complete problem. We show that we can approximate both single-criterion problems such as P _m |d _j |∑U _j as well as more challenging multi-criteria scheduling problems, like P _m ||C _max,∑C _j and P _m |d _j |C _max,∑C _j ,∑U _j . The latter problems are evaluated with extensive simulations comparing the standard multi-criteria evolutionary algorithm NSGA-2 and the new agent-based model.     

A strategy of knowledge elicitation for developing an integrated bidding/production management expert system for the precast industry

Bidding and production decisions, including the estimation of optimal mark-up on price, represent major decision problems for companies formulating a successful business strategy. The objective of this research was to develop a strategy for developing an integrated bidding/production management expert system to assess the suitability of incoming enquiries for a particular company and suggests a ‘bid/no bid’ decision. If a decision to bid is taken then the expert system should provide advice on the optimal mark-up to maximise the chance of winning potential contracts. The system developed in this paper is composed of an information system, that integrates design, estimation and production planning, and a knowledge-base to provide abstracted information and advice to managers in charge of bidding. The information system analyses the records of previous contracts and presents managers with vital information that minimises the risk of poor decisions associated with bidding. The knowledge-base is composed of intelligent rules which were elicited from previous contract records and experienced managers in charge of bidding. They are designed to advise managers on two major issues: bid/no bid and estimation of optimal tender price. A number of factors that affect bidding strategies were identified from reviewing previous bidding methodologies and surveying eight major companies in the UK by means of semi-structured interviews.     

A methodology for eliciting,modelling, and evaluating expert knowledge for an adaptive work-integrated learning system

We present a methodology for constructing and evaluating models for adaptive informal technology-enhanced workplace learning. It is designed for knowledge-intensive work domains which are not pre structured according to a fixed curriculum. We extend research on Competence-based Knowledge Space Theory which has been mainly applied in educational settings. Our approach employs systematic knowledge elicitation and practically feasible evaluation techniques performed as part of the modelling process for iterative refinement of the models. A case study was performed in the Requirements Engineering domain to apply and test the developed methodology. We discuss lessons learned and several implications for knowledge engineering for adaptive workplace learning.     

Specifying an expert system

The success of numerous expert systems in practical applications warrants a more formal approach to their development and evaluation. Reliability assurance of expert systems requires a methodology for the specification and evaluation of these systems. Expert systems are a new class of software system, but some traditional techniques of software development may be adapted to their construction. However, the specification of an expert system differs from that of a more traditional software program in that parts of the specification are permitted to be only partially described when development starts.

Specifications have two important purposes: as contracts between suppliers and clients, and as blueprints for implementation. A specification consists of a problem specification and a solution specification. The problem specification plays the role of contract and states explicitly what the problem to be solved is, and the constraints that the final product must satisfy. The solution specification plays the role of blueprint and has two major aspects: analyzing how a human expert solves the problem, and proposing an equivalent automated solution. We propose an approach to the specification of expert systems that is flexible, yet rigorous enough to cover the important features of a wide range of potential expert system applications. We describe fully each of the components of an expert system specification and we relate specification to the issues of evaluation and maintenance of expert systems.     

Development of an expert system for preliminary risk assessment of existing concrete dams

The paper presents an expert system to assist in the field inspection of existing concrete dams within the context of a preliminary risk assessment. The paper describes the engineering knowledge and reasoning required to conduct a deterministic field evaluation of the structural stability of the dam. The symptoms and failure modes identified by the expert system along with the required knowledge and procedures are organized in a structured knowledge tree. The instantiation of the frames and firing of the rules for each consultation traces part of the inference tree contained in the structured knowledge tree. Interaction between nearly decomposable problems are executed with metaknowledge procedures, shared rule groups, and active values. Examples are provided.     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

行业信息化知识库系统知识库设计

行业信息化知识库(KBI)有别于一般的知识库和专家系统,所以根据需求,采用了知识本体的知识表示形式,为了更合理的建设知识库,通过对行业信息化知识的行业结构分析,构建了知识模型,并依据知识表示形式以及构建的知识模型,设计了行业信息化知识库系统的知识库.该知识库是针对战略物资行业的,提出了知识库系统的总体结构,把知识库和数据库相结合,进行了知识库结构的逐步设计.     

Toward an expert system for chromosome analysis

Automation of chromosome analysis has long been considered as a very difficult task. Efforts to computerize some or all of the procedures using various conventional pattern recognition techniques have had only limited success. In this paper the previous work in this domain is briefly reviewed, with a discussion of the limitations of the existing approaches. An attempt to apply AI techniques is introduced, and the organization of an expert image analysis system for chromosome classification is described, mainly at a conceptual level. Based on the proposed architecture, the low-level processes (segmentation, feature extraction) and the high-level processes (classifications or interpretations) can be carried out in a knowledge-guided fashion with a combinational use of image processing and pattern recognition knowledge, as well as expert chromosome classification knowledge embedded in a rule-based structure. A knowledge-based chromosome image analysis scheme is presented which adopts a hierarchical hypothesize-and-verify paradigm. Example rules are given to illustrate how they can be used in this scheme.