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。     

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.     

Assessing problem solving in expert systems using human benchmarking

The human benchmarking approach attempts to assess problem solving in expert systems by measuring their performance against a range of human problem-solving performances. We established a correspondence between functions of the expert system GATES and human problem-solving skills required to perform a scheduling task. We then developed process and outcome measures and gave them to people of different assumed problem-solving ability. The problem-solving ability or “intelligence” of this expert system is extremely high in the narrow domain of scheduling planes to airport gates as indicated by its superior performance compared to that of undergraduates, graduate students and expert human schedulers (i.e. air traffic controllers). In general, the study supports the feasibility of using human benchmarking methodology to evaluate the problem-solving ability of a specific expert system.     

Trace-driven knowledge acquisition (TDKA) for rule-based real time scheduling systems

Expert scheduling systems, which develop the schedule automatically on a real time basis, are able to respond to the changes of product demand in Flexible Manufacturing Systems (FMS). While developing an expert scheduling system, the most time-consuming and difficult step is knowledge acquisition, the process that elicits the knowledge from experts and transfers it into the knowledge base. A trace-driven knowledge acquisition (TDKA) method is proposed to extract the expertise from the schedules produced by expert schedulers. Three phases are involved in the TDKA process: data collection, data analysis, and rule evaluation. In data collection, the expert schedulers are identified and decisions made during the scheduling process are recorded as a trace. In data analysis, a set of scheduling rules is developed based on the trace. The rules are then evaluated in the last phase. If the resulting rules do not perform as well as the expert schedulers, the process returns to phase two and refines the rules. The whole process stops whenever the resulting rules perform at least as well as the expert schedulers. A circuit board production line is used to demonstrate the feasibility of the TDKA methodology. The scheduling rules perform much better than the expert schedulers from whom the rules are extracted.     

Testability of expert systems in system development and application

In this paper the difficulties arising out of a necessary examination of expert systems as to the ‘correctness’ of functioning are outlined. The argumentation is based on the problematic use of the knowledge term in expert system development and the design perspectives connected with the cognitivistic knowledge concept. It becomes obvious that fundamental problems in system development will involve negative consequences for utilization. The perspective developed from this analysis is assuming that these problems have to be taken into account in development and have to be elucidated for utilization.     

Knowledge Acquisition Advisor (KA): An interactive knowledge-acquisition tool for expert system development

A new interactive knowledge acquisition tool, called Knowledge Acquisition Advisor (KA²), is presented in this paper. The new tool will help knowledge engineers to conduct effective knowledge-elicitation interviews with domain experts through structured knowledge acquisition for both analytic and synthetic problems. A graphic modeling data structure, called Knowledge Graph is proposed, which allows knowledge engineers to model domain problems with their images and understanding. By using Knowledge Graph, knowledge engineers are able to decompose a domain problem into several components, to model the feature of each component, and to explore their relations by linking them with sets of questions. These questions can later be employed to guide the KA interview. Moreover, KA² is particularly useful for interview through computer networks, so the knowledge acquisition can take place remotely.     

Hybrid expert systems: A survey of current approaches and applications

This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems’ areas. We also observe that many new industrial applications are developed using hybrid expert systems recently.     

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.     

Flexible control in expert systems for construction tasks

In most expert systems for constructional tasks, the knowledge base consists of a set of facts or object definitions and a set of rules. These rules contain knowledge about correct or ideal solutions as well as knowledge on how to control the construction process. In this paper, we present an approach that avoids this type of rules and thus the disadvantages caused by them.We propose a static knowledge base consisting of a set of object definitions interconnected by is-a and part-of links. This conceptual hierarchy declaratively defines a taxonomy of domain objects and the aggregation of components to composite objects. Thus, the conceptual hierarchy describes the set of all admissible solutions to a constructional problem. Interdependencies between objects are represented by constraints. A solution is a syntactically complete and correct instantiation of the conceptual hierarchy.No control knowledge is included in the conceptual hierarchy. Instead, the control mechanism will use the conceptual hierarchy as a guideline. Thus it is possible to determine in which respects a current partial solution is incomplete simply by syntactical comparison with the conceptual hierarchy. The control architecture proposed here has the following characteristics: separation of control and object knowledge, declarative representation of control knowledge, and explicit control decisions in the problem solving process. Thus, a flexible control mechanism can be realized that supports interactive construction, integration of case-based approaches and simulation methods.This control method is part of an expert system kernel for planning and configuration tasks in technical domains. This kernel has been developed at the University of Hamburg and is currently applied to several domains.     

Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective

Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.     

An introduction to expert systems

The paper considers the features of expert systems which make them of educational interest, and discusses issues of knowledge representation through rule sets, and how such systems can be used to give explanation and advice. Working systems are outlined in Medicine, Geology and Computing. Some limitations of expert systems are noted and their future potential assessed.     

Institutionalizing expert systems: Guidelines and legal concerns

Often, knowledge engineers become so involved in the development process of the expert system that they fail to look further down the road toward the expert system's institutionalization within the organization. Institutionalization is an important component of the expert system planning process. More specifically, the legal issues associated with expert systems development and deployment are critical institutionalization factors. This paper looks at some expert system institutionalization guidelines, and then focuses on legal considerations. Adapted from Zeide, J. S. and J. Liebowitz, “Institutionalizing Expert Systems: Guidelines and Legal Concerns,” Proceedings of FLAIRS-92 Conference, Ft. Lauderdale, FL.     

High Performance Knowledge Bases: four approaches to knowledge acquisition, representation and reasoning for workaround planning

As part of the DARPA-sponsored High Performance Knowledge Bases program, four organisations were set the challenge of solving a selection of knowledge-based planning problems in a particular domain, and then modifying their systems quickly to solve further problems in the same domain. The aim of the exercise was to test the claim that, with the latest AI technology, large knowledge bases can be built quickly and efficiently. The domain chosen was ‘workarounds’; that is, planning how a convoy of military vehicles can ‘work around’ (i.e. circumvent or overcome) obstacles in their path, such as blown bridges or minefields.

This paper describes the four approaches that were applied to solve this problem. These approaches differed in their approach to knowledge acquisition, in their ontology, and in their reasoning. All four approaches are described and compared against each other. The paper concludes by reporting the results of an evaluation that was carried out by the HPKB program to determine the capability of each of these approaches.     

A Delphi-based approach to developing expert systems with the cooperation of multiple experts

Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the “time” factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.     

Knowledge based formation of re-configurable assembly cells

When developing assembly cells with highly complex modular structures, designers need to translate user requirements into a set of design rules and potential cell configurations. The success in matching user requirements to potential products is dependent on how well the functional and non-functional customer requirements can be understood and translated into cell features (design rules, processes and module types). This paper reports on a knowledge based methodology for forming customisable re-configurable assembly cells. The approach is based on matching user requirements to existing supplier knowledge in terms of design rules and principles, modules offered by different vendors, new emerging technologies and existing own and competitors’ products. The decision making includes requirements analysis, generating assembly processing alternatives and evaluating and selecting assembly modules and cells. The proposed approach aims to assist decision making in assembly system design by enabling users and suppliers to jointly participate in an interactive and iterative process of forming re-configurable assembly cells.     

Automated knowledge acquisition for the surface-mount PWB assembly domain

The development of knowledge-based (or expert) systems for the surface-mount printed wiring board (PWB) assembly domain requires the understanding and regulation of several complex tasks. While the knowledge base in an expert system serves as a storehouse of knowledge primitives, its design and development is a bottleneck in the expert system development life-cycle. Therefore the development of an automated knowledge acquisition (KA) facility (or KA tool) would facilitate the implementation of expert systems for any domain. This paper describes an automated KA tool that helps to elicit and store information in domain-specific knowledge bases for surface-mount PWB assembly. A salient feature of this research is the acquisition of uncertain information.     

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.     

Two approaches to developing expert systems: A consideration of formal and semi-formal domains

The conventional approach to developing expert systems views the domain of application as being formally defined. This view often leads to practical problems when expert systems are built using this approach. This paper examines the implications and problems of the formal approach to expert system design and proposes an alternative approach based on the concept of semi-formal domains. This approach, which draws on the work of socio-technical information systems, provides guidelines which can be used for the design of successful expert systems.     

Towards expert systems for telecommunications policy analysis

Telecommunications policy analysis is currently a highly complex arena of debate. Systems have been developed by British Telecom which replace traditional simulation models. The paper reviews the progress to date within BT, discusses topics for future research and suggests what can be usefully gained from the application of expert systems to policy analysis and evaluation.     

煤矿瓦斯预测专家系统中基于粗集的知识获取方法

针对现有煤矿瓦斯预测专家系统因没有新知识获取措施及知识自更新功能而预测效果不佳的问题,提出了基于粗集的知识获取方法。该方法首先建立瓦斯数据与瓦斯突出强度之间关系的预测样本集;然后运用粗糙集的连续属性离散化、属性约简以及规则提取算法,从大量的预测样本集中自动获取预测知识,并将预测知识存储于专家系统知识库中;最后基于推理机实现煤矿瓦斯突出的实时预测。实例分析验证了该方法在煤矿瓦斯突出预测专家系统知识获取中的有效性和实用性。