基于场景及规则获取模板的知识获取方法

产生式规则作为知识库系统进行推理的常用的、可读性好的知识表示形式,在构建知识库系统时有极大的优越性.提出一种基于场景及规则获取模板的知识获取方法,并以某高分子复合材料的加工专家为知识获取对象.该方法通过分析、记录领域专家进行设计的过程、解决问题的过程和动作,将领域问题按层次细化为一系列子问题,并在子问题场景下结合场景模型及知识获取模板来获取规则性知识.采用该方法可以辅助领域专家在明晰领域知识结构的基础上,逐步挖掘领域中细粒度的规则性知识.     

Compositional Bayesian modelling for computation of evidence collection strategies

As forensic science and forensic statistics become increasingly sophisticated, and judges and juries demand more timely delivery of more convincing scientific evidence, crime investigation is becoming progressively more challenging. In particular, this development requires more effective and efficient evidence collection strategies, which are likely to produce the most conclusive information with limited available resources. Evidence collection is a difficult task, however, because it necessitates consideration of: a wide range of plausible crime scenarios, the evidence that may be produced under these hypothetical scenarios, and the investigative techniques that can recover and interpret the plausible pieces of evidence. A knowledge based system (KBS) can help crime investigators by retrieving and reasoning with such knowledge, provided that the KBS is sufficiently versatile to infer and analyse a wide range of plausible scenarios. This paper presents such a KBS. It employs a novel compositional modelling technique that is integrated into a Bayesian model based diagnostic system. These theoretical developments are illustrated by a realistic example of serious crime investigation.     

KNOWLEDGE ACQUISITION TECHNIQUES AND SECOND-GENERATION EXPERT SYSTEMS

We present techniques used in ADELE, a second-generation expert system (SGES), to support the knowledge acquisition activity in the diagnostic domain. The approach has been studied inside the framework of SGES; it is based on the reunions between knowledge acquisition and explanations. When new heuristic knowledge is acquired, its justifications are looked for in domain models to support the knowledge acquisition process. ADELE is a medical diagnostic reasoning system for electromyography.     

Contextualized access to knowledge: theoretical perspectives and a process-tracing study

This paper investigates the theoretical foundations and empirical evidence concerning contextualized access to task domain knowledge enabled by hypertext-style links. It examines several relevant theoretical perspectives, including theories of discourse comprehension, contextualized learning and the production paradox, and reports on an exploratory study in the knowledge-based systems (KBS) domain. Process-tracing data was collected using a 'thinking-aloud' procedure, and data analysis focused on some highly illustrative verbal protocols. Results indicate that contextualized access to domain knowledge can be critical for understanding KBS output, and that lack of it can cause comprehension difficulties. Contextualized access is highly effective for resolving comprehension difficulties arising from the users' lack of task domain knowledge and for reducing the motivational 'cost' of learning. We conclude that it has the potential for substantially increasing the effectiveness of information systems.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

On the use of shared task models in knowledge acquistion,strategic user interaction and clarification agents

In this paper, three different roles of a shared task model as an intermediate representation of a task are presented and illustrated by applications developed in cooperation with industry. First the role of a shared task model in knowledge acquisition is discussed. In one of the two applications, decision support in the domain of soil sanitation, one of the existing generic task models for diagnostic reasoning provided a means to structure knowledge acquisition. In the second application, diagnosis of chemical processes, the acquisition process resulted in a shared task model for diagnostic reasoning on Nylon-6 production. Secondly, the role of a shared task model in designing user interaction is addressed. Three levels of interaction are considered of importance: interaction at the object level, at the level of strategic preferences, and at the level of task modification. In an application in the domain of environmental decision making, this led to the design of a user interface based on the acquired shared task model, within which all three levels of interaction were available to users. Finally, the role of shared task models within a multi-agent system including a clarification agent is addressed. Two software agents were designed that each share a task model with the user: one for a diagnosis task, and one for a clarification task. The shared model of the clarification task reflects the shared task model of diagnosis; clarification includes clarification of the overall diagnostic reasoning process. The multi-agent architecture presented has been developed to support a user both at the level of the diagnostic task he or she is performing and at the level of clarification. The architecture has been applied to the diagnosis of chemical processes.     

Knowledge-based systems from a socio-cognitive 1 perspective

. Recent trends in the design and development of knowledge-based systems KBSs are discussed with special emphasis on issues that relate to situated knowledge. A knowledge base is regarded as a model of expertise that acknowledges the embeddedness of expert knowledge in social interaction and in the workplace in general. KBS development is viewed as an instance of socio-technical design. Experience from several European projects is recounted to illustrate the issues addressed. Suggestions for KBS development are presented as methodological guidelines, with special emphasis on systems employing case-based reasoning.     

A hybrid system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.     

Knowledge-based systems and knowledge management: Friends or foes?

Knowledge-based systems (KBS) provide a way of formalizing and automating knowledge. Their worth for managing the knowledge assets has not gone unnoticed: they have been promoted as safeguards to retain expert knowledge, to avoid knowledge erosion, etc. KBS are the outcome of a knowledge engineering process that may be seen as providing some of the building blocks of knowledge management. Although `knowledge' is the first word in knowledge-based systems, they are hardly ever considered from a knowledge perspective. As a result, a biased view of the organizational value of KBS exists in the literature, putting an undue emphasis on technology. The key issue addressed in this article is: how does knowledge engineering relate to a broader perspective of knowledge management? A way to identify the issues to be addressed when valuing KBS as potential measures for knowledge management is presented. To illustrate its value, the outcomes of a recent empirical investigation of how KBS function within organizations are presented.     

Knowledge-based systems from a socio-cognitive 1 perspective

Abstract

Recent trends in the design and development of knowledge-based systems KBSs are discussed with special emphasis on issues that relate to situated knowledge. A knowledge base is regarded as a model of expertise that acknowledges the embeddedness of expert knowledge in social interaction and in the workplace in general. KBS development is viewed as an instance of socio-technical design. Experience from several European projects is recounted to illustrate the issues addressed. Suggestions for KBS development are presented as methodological guidelines, with special emphasis on systems employing case-based reasoning.     

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.     

Processing of domain experts' knowledge for development of a KBS for risk analysis

Knowledge-based systems (KBSs) are being widely used for risk analysis of structures. The KBS makes use of domain experts' knowledge, which is an essential element of input for risk analysis. Acquired pieces of knowledge must be processed and improved with the help of domain experts before use for risk analysis. Utilization of the acquired knowledge without processing can reduce the quality of risk analysis. Processing of the knowledge elicited from experts is essential to identify the conflicting pieces of knowledge, gaps, and redundancies. Further, it is necessary to eliminate conflicts and represent the knowledge in a consistent and complete manner suitable for carrying out risk analysis. Processing of the knowledge elicited from different domain experts is a difficult task. A new approach using graph theoretic technique is proposed in this paper for the processing of knowledge and its use in the creation of a knowledge base. This new approach helps in developing an effective KBS for risk analysis of structures. To demonstrate the approach, an example problem of risk analysis of roof structure against damage due to cyclonic winds is considered. © 2001 John Wiley & Sons, Inc.     

Towards a Knowledge-Level Model of Context and Context Use in Diagnostic Problems

Diagnostic support systems that help solving problems in open and weak theory domains need to be context-sensitive in order to reveal flexible and efficient behaviour. This paper presents a task-oriented methodology for analysing and modeling contextual knowledge at the knowledge level. We present a context-sensitive diagnosis approach (ConSID) which clarifies the connection between content and process knowledge. The former embodies the domain model, while the latter embodies the task and method models. We present a prototypical system, the ConSID-Creek, that applies the ConSID approach to the medical diagnostic domain. We illustrate how the system integrates case-based and explanation-based reasoning paradigms when realizing the abductive subtask of the overall diagnostic task.     

On making expert systems more like experts

Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem-solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.     

ISTAR — a tool for creative design of knowledge bases

This paper gives a comprehensive explanation of the Istar knowledge representation software tool. Not only does it describe the features and facilities found in Istar, but it discusses why they are as they are.
Istar is one of a new generation of knowledge representation tools, aimed at ill-structured domains of knowledge. While it can be used in traditional KBS projects, in which pieces of knowledge from a domain expert are assembled to form a working knowledge base, it is designed for situations in which there is a large element of creative design: knowledge refinement and generation resulting from the knowledge representation process.
The knowledge representation 'language' is purely graphical; the knowledge engineer 'draws' knowledge on an easel as a box and arrows diagram. Behind this diagram is the knowledge base itself, in the form of integrated inference nets, Bayesian nets and semantic nets. This paper discusses the reasons for these design choices and, briefly, some of the issues faced in development of Istar.     

Computer-Assisted Diagnosis of Postmenopausal Osteoporosis Using a Fuzzy Expert System Shell

This paper shows how the AMDIS (Automated Medical Diagnosis with Intelligent Systems) integrated system can be employed to build a fuzzy medical expert system in the domain of postmenopausal osteoporosis, The fundamental aims of the expert system are to standardize knowledge and support physicians in the early detection of postmenopausal osteoporosis. A wide range of diagnostic situations has been considered for both categories of the disease, with judgments that range from disease is excluded to disease is definite. The salient aspects of the approach are the use of fuzzy logic as an analytic language for the representation and manipulation of knowledge and strategies and the integration of structured interview techniques and learning-by-example to address the knowledge acquisition task.     

Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems

Abstract

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.     

.基于知识系统的不确定性模型

研究基于知识系统（KBS）中知识的不确定性是开发KBS的重要问题,但现有模型化KBS几乎都是基于确定性知识的。以经典的CommonKADS模型为背景,采用模型化工程中的不确定性技术,研究KBS中不确定性知识的表示方法。首先在基于值系统的值集概念上引入假设函数集合的评估函数,定义静态不确定性领域知识;而后采用因果模型描述动态的不确定性推理知识和任务知识;最后将三类不确定性知识映射至CommonKADS模型。由此给出了描述不确定KBS的通用模型,扩展了KBS的可用性。     

SYSTEMATIC KNOWLEDGE BASE DESIGN FOR MEDICAL DIAGNOSIS

Experienced diagnosticians draw on a rich variety of reasoning techniques, ranging from the association of symptoms and diseases to causal reasoning about disease mechanisms and first-principle analysis grounded in basic science. The entire range of diagnostic reasoning strategies is also necessary for a computer program to be truly proficient and robust. The development of such a program has been impeded by the inherent complexity of the domain and the consequent lack of an adequate methodology for knowledge organization and integration. We present a methodology for structuring medical knowledge and managing its complexity. We illustrate this methodology in the context of an experimental knowledge base in the domain of jaundice. We believe that this systematic knowledge base design will support the development of automated reasoning methods that span the entire range of reasoning techniques used by physicians.     

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.