Automated knowledge acquisition for design and manufacturing: The case of micromachined atomizer

In order to remain competitive in the global market, original equipment manufacturers (OEMs) are developing a process-based, knowledge-driven product development environment with emphasis on the acquisition, storing, and utilization of manufacturing knowledge. This is usually achieved by using the symbolic artificial intelligence (AI) approach. Specifically, knowledge-based expert systems are developed to capture human expertise, mostly in terms of IF–THEN production rules. It has been recognized that the development of symbolic knowledge-based expert systems suffers from the so-called knowledge acquisition bottleneck. Knowledge acquisition is the process of collecting domain knowledge and transforming the knowledge into a computerized representation. It is a challenging and time-consuming process due to the difficulties involved in eliciting knowledge from human experts. This paper presents an automated approach for knowledge acquisition by integrating neural networks learning ability and fuzzy logics structured knowledge representation. Using this approach, knowledge is automatically acquired from data and represented using humanly intelligible fuzzy rules. The approach is applied to a case study of the design and manufacturing of micromachined atomizers for gas turbine engine. The influence of geometric features on the performance of the atomizers is investigated. The results are then compared with those obtained using traditional regression analysis approach (abstract mathematical models). It was found that the automated approach provides an efficient means for knowledge acquisition. Since the fuzzy rules extracted are easy to understand, they can be used to allow more clear specification of manufacturing processes and to shorten learning curves for novice manufacturing engineers.     

Explanation-based learning as justification of knowledge

Abstract: This paper describes the use of the explanation-based learning (EBL) machine learning technique in the practical domain of knowledge acquisition for expert systems. A knowledge acquisition tool, EBKAT (Explanation-Based Knowledge Acquisition Tool), is described, which may be used in the development of knowledge bases for diagnostic expert systems. The functioning of EBKAT attempts to combine the full potential of a domain expert's skills and the power of explanation-based machine learning techniques. The EBL component is not employed in the acquisition of the knowledge base rules but is used to justify the knowledge entered and to relate it to the knowledge already in the system. It is suggested that the EBKAT tool goes some way towards overcoming the knowledge acquisition bottleneck and results in the acquisition of knowledge which is rich in contextual information.     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

Explanation-Based Learning for Diagnosis

We present explanation-based learning (EBL) methods aimed at improving the performance of diagnosis systems integrating associational and model-based components. We consider multiple-fault model-based diagnosis (MBD) systems and describe two learning architectures. One, EBLIA, is a method for learning in advance. The other, EBL(p), is a method for learning while doing. EBLIA precompiles models into associations and relies only on the associations during diagnosis. EBL(p) performs compilation during diagnosis whenever reliance on previously learned associational rules results in unsatisfactory performance—as defined by a given performance threshold p. We present results of empirical studies comparing MBD without learning versus EBLIA and EBL(p). The main conclusions are as follows. EBLIA is superior when it is feasible, but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required.     

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.     

Computer-assisted localization of nervous system injuries

Expert systems to assist in neurological diagnosis require a representation of anatomical relationships. In order to test one representational method, a prototype expert system was developed. It accepts patient signs of neurological dysfunction and identifies the site of nervous system injury. The system's knowledge base is contained in a semantic network which represents nervous system anatomy and the physical signs of injuries. When provided with an individual's physical signs, the network is searched by a simple algorithm; the anatomical locations which best explain the physical signs are the system's output. Medical expert systems which require anatomical reasoning can use a direct representation of spatial relationships to avoid the difficulties of encoding clinical associations in the form of If-Then rules.     

Collection, storage and application of human knowledge in expert system development

Abstract: Expert systems are programs that analyze data by mimicking the thought processes of an expert. Two expert systems were developed by the Reservoir Evaluation and Advanced Computational Techniques group to aid in oil prospecting for two New Mexico formations, leading to the development of a third customizable fuzzy expert system. Knowledge engineering is a major part of the development of these expert systems, in which expert knowledge is solicited, analyzed, converted to rules stored in a system's knowledge base, and used by the computer to produce expert judgment. Numerical versions of the rules are used to analyze data and produce an evaluation of the user's prospect. In addition, the knowledge base preserves expert knowledge for future workers. This is especially important in the petroleum industry, as there is a cyclical trend in employment relating to the price of oil, retirements and people leaving and entering the industry.     

LUBRES: An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process

An expert system LUBRES (LUBricating oil Refining Expert System) is introduced in this paper. It helps plant operators in monitoring and diagnosing of abnormal situations in refining process of lubricating oil. The LUBRES structure, the knowledge base, and the inference machine are presented in detail. A new strategy is proposed for conflicts resolution – the sorting strategy of antecedents, and a selection strategy of knowledge rules in the memory knowledge base. Knowledge acquisition mechanism is based on an empirical knowledge table, while knowledge verification is carried out based on the directed graph approach. C++Builder and SQL Server 2000 have been used in developing the proposed system. LUBRES has been successfully implemented in Microsoft Windows Server environment. For 1 year, LUBRES has been used for monitoring and diagnosing of refining process of the lubricating oil. The industrial application of LUBRES proved its high reliability and accuracy.     

A self-learning expert system for diagnosis in traditional Chinese medicine

A novel self-learning expert system for diagnosis in Traditional Chinese medicine (TCM) was constructed by incorporating several data mining techniques, mainly including an improved hybrid Bayesian network learning algorithm, Naı̈ve–Bayes classifiers with a novel score-based strategy for feature selection and a method for mining constrained association rules. The data-driven nature distinguished the system from those existing TCM expert systems based on if-then rules to address knowledge elicitation problem. Moreover, the learned knowledge was provided in multiple forms including causal diagram, association rule and reasoning rules derived from classifiers. Finally, five representative cases were diagnosed to evaluate the performance of the system and the encouraging results were obtained. The results show that the prototype system performs well in diagnosis of TCM, and could be expected to be useful in the practice of TCM.     

Learning expert systems by being corrected

This paper describes a new method of knowledge acquisition for expert systems. A program, KABCO, interacts with a domain expert and learns how to make examples of a concept. This is done by displaying examples based upon KABCO's partial knowledge of the domain and accepting corrections from the expert. When the expert judges that KABCO has learnt the domain completely a large number of examples are generated and given to a standard machine learning program that learns the actual expert system rules. KABCO vastly eases the task of constructing an expert system using machine learning programs because it allows expert system rule bases to be learnt from a mixture of general (rules) and specific (examples) information. At present KABCO can only be used for classification domains but work is proceedings to extend it to be useful for other domains. KABCO learns disjunctive concepts (represented by frames) by modifying an internal knowledge base to remain consistent with all the corrections that have been entered by the expert. KABCO's incremental learning uses the deductive processes of modification, exclusion, subsumption and generalization. The present implementation is primitive, especially the user interface, but work is proceeding to make KABCO a much more advanced knowledge engineering tool.     

汽车故障诊断专家系统关键技术的研究与发展

在收集整理大量国内外相关研究文献的基础上,针对知识获取方法、知识表示方法及推理策略等这些关键技术相应的解决方法进行了分析,包括对传统方法的改进,如故障规则的自动获取、组合的知识表示方法和推理方法的多样化等,以及新理论新技术的应用,如基于案例的专家系统、基于模糊的专家系统、基于神经网络的专家系统以及基于行为的专家系统等,并提出智能化、网络化和集成化是未来故障诊断专家系统的发展方向。     

基于CLIPS的智能无人飞艇故障诊断专家系统开发

针对智能无人飞艇的故障诊断问题,设计开发了一套基于CLIPS框架的故障诊断专家系统。首先,根据诊断专家的知识进行故障分类并建立故障树;其次,基于CLIPS工具设计了智能无人飞艇的故障事实库和规则库。然后采用静态链接的方式将CLIPS框架嵌入到C++中,并设计了"路由跳转"功能,实现了用户输入与CLIPS的数据交换接口,并利用MFC框架开发了相应人机交互界面。该智能无人飞艇故障诊断专家系统的开发,改善了现阶段人工故障诊断的不规范及效率低下等问题,为智能无人飞艇的故障诊断、分析和排除提供了平台和支持技术。     

专家系统中基于粗集的知识获取、更新与推理

知识获取、知识更新和不确定性推理是设计专家系统的重要方面。根据粗集理论，提出了一种专家系统的结构模型，该系统在规则获取的基础上，利用系统运行的实例增量式地更新知识库中的规则及其参数，以改善系统的性能，利用知识库中的规则及数量参数进行不确定性推理，得出结论的可信度。     

汽轮发电机组故障诊断专家系统的研究

建立一个基于知识的汽轮发电机组故障诊断专家系统ＫＢＦＤＥＳ，该系统采用“框架＋规则”知识表示技术，并将框架驻留在内存，而将规则驻留在虚拟盘上；在诊断过程中采用广义的不精确推理策略，并对重要信息用一个组合神经网络进行智能识别；系统将神经网络技术和ＩＤ３算法结合，可以实现从诊断实例自动获取知识。     

Hierarchical model-based diagnosis for high autonomy systems

Deep reasoning diagnostic procedures are model-based, inferring single or multiple faults from the knowledge of faulty behavior of component models and their causal structure. The overall goal of this paper is to develop a hierarchical diagnostic system that exploit knowledge of structure and behavior. To do this, we use a hierarchical architecture including local and global diagnosers. Such a diagnostic system for high autonomy systems has been implemented and tested on several examples in the domain of robot-managed fluid-handling laboratory.Research supported by NASA-Ames Co-operative Agreement No. NCC 2-525, A Simulation Environment for Laboratory Management by Robot Organization.     

Incorporating statistical information into expert classification systems to reduce classification costs

Interactive expert systems seek relevant information from a user in order to answer a query or to solve a problem that the user has posed. A fundamental design issue for such a system is therefore itsinformation-seeking strategy, which determines the order in which it asks questions or performs experiments to gain the information that it needs to respond to the user. This paper examines the problem of optimal knowledge acquisition through questioning in contexts where it is expensive or time-consuming to obtain the answers to questions. An abstract model of an expert classification system — considered as a set of logical classification rules supplemented by some statistical knowledge about attribute frequencies — is developed and applied to analyze the complexity and to present constructive algorithms for doing probabilistic question-based classification. New heuristics are presented that generalize previous results for optimal identification keys and questionnaires. For an important class of discrete discriminant analysis problems, these heuristics find optimal or near-optimal questioning strategies in a small fraction of the time required by an exact solution algorithm.     

Constructing a nutrition diagnosis expert system

This paper presents a research of constructing a web-based expert system for nutrition diagnosis by utilizing the expert system techniques in artificial intelligence. The research implements Nutritional Care Process and Model (NCPM) defined by American Dietetic Association (ADA) in 2008 and integrate the nutrition diagnosis knowledge from dietetics professionals to establish the basics of building the rule-based expert system with its knowledge base. The system is built using Microsoft Visual Studio 2008 on .NET Framework 3.5SP1 utilizing the built in rule engine which comes with Windows Workflow Foundation.With the help of this system, it is easier for dietetics professionals to adapt to the newly introduced concept of nutrition diagnosis. At the heart of the web based expert system is a knowledge base, it has a rule engine which contains the nutrition diagnosis rules converted from signs and symptoms for nutrition diagnosis from dietetics professionals and are expressed in XML format which are then stored in a SQL database. A knowledge engineer will be able to use a rule editor to add new rules or to update existing rules within the rule database. Dietetics professionals would be able to enter patient’s basic data, anthropometric data, physical exam findings, biochemical data, and food/nutrition history into the program. After dietetics professionals complete nutrition assessment, the program will make inference to the rule base and make nutrition diagnosis. Dietetics professionals could then make the final diagnosis decision for the patient based on the diagnosis report generated by the web based nutrition diagnosis expert system.For this study, I have selected 100 chronic kidney disease patients under hemodialysis from a university hospital, recorded their albumin, cholesterol, creatinine before dialysis, height, and dry weight and then use these data to perform nutrition diagnosis with both the expert system and a practicing dietitian. After comparing the result, I found that the expert system is faster and more accurate than human dietitian.     

SMR (simulating medical reasoning): an expert shell for non-AI experts

SMR is an expert system shell designed to put the tools for knowledge acquisition directly into the hands of the domain expert. Since the knowledge base is represented as free text within a simplified syntactic structure, it is intelligible to anyone familiar with medical terminology. The knowledge base includes the rules for inference making as well as data groupings and protocols to facilitate case recording. In this paper, SMR is presented from the expert's point of view, describing the rule syntax and procedures for formulating the required diagnostic or therapeutic knowledge in the chosen domain. Similarly, the end-user's application of the system to patient data and the provisions for exploring and explaining the system's conclusions and reasoning processes are detailed. Avoiding tedious and often inane dialog, the user enters all that is known about the patient and receives a report of the system's conclusions and recommendations followed by a list of observations to be made in patient follow-up. An expert system for evaluating the diabetic patient is used to illustrate system operations.     

ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system

This paper describes ROGET, a knowledge-based system that assists a domain expert with an important design task encountered during the early phases of expert-system construction. ROGET conducts a dialogue with the expert to acquire the expert system's conceptual structure, a representation of the kinds of domain-specific inferences that the consultant will perform and the facts that will support these inferences. ROGET guides this dialogue on the basis of a set of advice and evidence categories. These abstract categories are domain independent and can be employed to guide initial knowledge acquisition dialogues with experts for new applications. This paper discusses the nature of an expert system's conceptual structure and describes the organization and operation of the ROGET system that supports the acquisition of conceptual structures.