Use of a domain model to drive an interactive knowledge-editing tool

The manner in which a knowledge-acquisition tool displays the contents of a knowledge base affects the way users interact with the system. Previous tools have incorporated semantics that allow knowledge to be edited in terms of either the structural representation of the knowledge or the problem-solving method in which that knowledge is ultimately used. A more effective paradigm may be to use the semantics of the application domain itself to govern access to an expert system's knowledge base. This approach has been explored in a program called OPAL, which allows medical specialists working alone to enter and review cancer treatment plans for use by an expert system called ONCOCIN. Knowledge-acquisition tools based on strong domain models should be useful in application areas whose structure is well understood and for which there is a need for repetitive knowledge entry.     

Automatic refinement of knowledge bases with fuzzy rules

The knowledge-acquisition bottleneck obstructs the development of expert systems. Refinement of existing knowledge bases is a subproblem of the knowledge-acquisition problem. The paper presents a HEuristic REfinement System (HERES), which refines rules with mixed fuzzy and nonfuzzy concepts represented in a variant of the rule representation language Z-II automatically. HERES employs heuristics and analytical methods to guide its generation of plausible refinements. The functionality and effectiveness of HERES are verified through various case studies. It has been verified that HERES can successfully refine knowledge bases. The refinement methods can handle imprecise and uncertain examples and generate approximate rules. In this aspect, they are better than other famous learning algorithms such as ID3^15–18, AQ11, and INDUCE^{14, 19, 20} because HERES' methods are currently unique in processing inexact examples and creating approximate rules.     

A conceptual design aid environment for expert-database systems

With the actual penetration of expert systems into the business world, the question is, how the expert system idea can be used to enhance the existing information systems with more intelligence in usage and operation. This interest is not surprising due to the advancement of the fifth generation of computer technology, and avid interest in the field of Artificial Intelligence. Therefore design of an information system for an application becomes more complex, and the inability of the human designer to deal with it increases. For designing intelligent systems, we have to be able to forecast the behavior of the information system more precisely before implementing it, i.e. we'have to support the specification process.Clearly the technology, such as Data base systems, is leading on efficiency issues as those needed for the construction, retrieval and manipulation of large shared data base. On the other hand, the AI techniques have improved significantly with function such as deductive reasoning and natural language processing. It is important to find way to merge these technologies into one mainstream of computing. A meeting point for the two areas is the issue of conceptual knowledge modelling, so that models can be created that will define the role and the ways to use data in AI systems. In the framework of this study, one possible expert system design aid environment has been suggested to assist the designer in his work.In a conceptual modelling environment a model is given for analysing complex real world problems known as the Conceptual Knowledge Model (CKM), represented by a Graphical and a Formal Representation. The Graphical Representation consists of three graphs: Conceptual Requirement Graph, Conceptual Behavior Graph, and Conceptual Structure Graph. These graphs are developed by involving the expert during the design process. The graphs are then transformed into first-order predicate logic to represent the logical axioms of a theory, which constitutes the knowledge base of the Expert System. The model suggested here is a step towards closing the gap between the theory of the conventional data base theory and AI databases.     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

Computerized conferencing for knowledge acquisition from multiple experts

The increasing complecity of many expert system application areas calls for the integration of the knowledge of multiple experts. The use of multiple experts introduces some interesting new problems during the process of knowledge acquisition. The problems are further complicated when the experts are geographically dispersed or unavailable for face-to-face interactions.

This article discusses the motivations for acquiring the knowledge of multiple experts, the problems related to knowledge acquisition, new issues that arise whens multiple experts interact, solutions that can be brought to bear in building multiple expert systems (particularly when experts are geographically dispersed), and new tools for knowledge engineers to use when dealing with multiple experts.     

REX: Robotics expert systems building tool

REX, an expert systems development shell for robotics applications, is presented in the paper. The shell is intended for building expert systems in various domains of robotics, including design, planning, control, fault-detection, and navigation. REX's knowledge and data representation techniques include several standard techniques, like rules, frames, parameters, and variables, as well as certain techniques which are not commonly used by other expert systems building tools. These specific techniques include sensor data, status data, models and algorithms. The REX inference engine provides a means for reasoning with all of these knowledge and data types. The inference engine features both off-line and on-line modes of operation. REX has powerful interfaces for communicating both with the user and with other software modules in a complex application. Certain learning techniques are built into the shell. An example of applying REX for building an expert system in the domain of robot control is also presented. Finally, a discussion is provided on the relation between REX and second-generation expert systems.     

Sequential decision-theoretic models and expert systems

Sequential decision models are an important component of expert systems since, in general, the cost of acquiring information is significant and there is a trade-off between the cost and the value of information. Many expert systems in various domains (business, engineering, medicine etc.), needing costly inputs that are not known until the system operates, have to face this problem. In the last decade the field of sequential decision models based on decision theory (sequential decision-theoretic models) have become more and more important due to both the continuous progress made by research in Bayesian networks and the availability of modern powerful tools for building Bayesian networks and for probability propagation. This paper provides readers (especially knowledge engineers and expert system designers) with a unified and integrated presentation of the disparate literature in the field of sequential decision-making based on decision theory, in order to improve comprehensibility and accessibility. Besides the presentation of the general theory, a view of sequential diagnosis as an instance of the general concept of sequential decision-theoretic models is also shown.     

The role of the industrial engineer in developing expert systems

The most popular area of Artificial Intelligence application today is in expert systems. This paper contains a discussion of expert systems, otherwise known as knowledge-based systems and knowledge systems. The principal components of an expert system, and the evolution of expert systems are presented. The suitability of a task to an expert system is proposed. When a task is suitable for an expert system application, the system must be developed by a knowledge engineer. The methodology that the knowledge engineer must go through to develop an expert system is demostrated. Industrial engineers have formal training in many areas which can be useful when assumming the role of knowledge engineer. These areas of industrial engineering and how they are beneficial is discussed. What the future may hold in store is also pondered.     

EXPERT-MM: A knowledge-based system for maintenance management

This paper presents an approach to the design and development of knowledge-based systems in general and their application in the field of maintenance management in particular. Our approach is based on the idea that different kinds of knowledge in a given domain, namely declarative, procedural and heuristic are supported by corresponding methods and software tools. A prototype knowledge-based system, called EXPERT-MM, for the maintenance activities in the Siam Gipsum Industry (Bangkok, Thailand) has been worked out as a case study and is described in the paper. EXPERT-MM supports three main functions: maintenance policy suggestions, machine diagnosis and maintenance scheduling. The maintenance policy deals with the three types of preventive maintenance. For each component of the equipment it analyses the historical failure data and recommends an appropriate policy with optimal preventive maintenance intervals. This is based on the experts' knowledge stored in a knowledge base. A rotary screw type air compressor is selected for a diagnosis. The knowledge representation scheme is rule-based and the inference strategy mechanism is backward chaining. The knowledge-acquisition process has been organized and realised using a decision tree diagram. The knowledge base contains 154 rules for the diagnosis and 54 rules for the maintenance model selection. the maintenance scheduling module is procedure based. EXPERT-MM development is based on the software tools dBase III Plus, TURBO PASCAL version 6.0 and expert system shell EXSYS, all integrated into a single software system with a user-friendly interface.     

Support logic programming

This article describes a support logic programming system which uses a theory of support pairs to model various forms of uncertainty. It should find application to designing expert systems and is of a query language type like Prolog. Uncertainty associated with facts and rules is represented by a pair of supports and uses ideas from Zadeh's fuzzy set theory and Shafer's evidence theory. A calculus is derived for such a system and various models of interpretation given. the article provides a form of knowledge representation and inference under uncertainty suitable for expert systems and a closed world assumption is not assumed. Facts not in the knowledge base are uncertain rather than assumed to be false.     

Knowledge acquisition and interface design

Describes tools, techniques, and concepts to optimize user interfaces. The best way to ensure that a software system is friendly and works is to base it on the intended users' mental models (how they view the world), knowledge structures (what they know and how they have organized it), and work processes. The author uses a team of engineers to systematically acquire and analyze user and domain knowledge and to translate that knowledge into user-interface design decisions. This front-end analysis method, combined with knowledge-acquisition techniques, lets one build user-centered systems     

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.     

COMBINING KNOWLEDGE BASES CONSISTING OF FIRST-ORDER THEORIES

Consider the construction of an expert system by encoding the knowledge of different experts. Suppose the knowledge provided by each expert is encoded into a knowledge base. Then the process of combining the knowledge of these different experts is an important and nontrivial problem. We study this problem here when the expert systems are considered to be first-order theories. We present techniques for resolving inconsistencies in such knowledge bases. We also provide algorithms for implementing these techniques.     

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 interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.     

rCOS: a formal model-driven engineering method for component-based software

Model-driven architecture (MDA) has become a main stream technology for software-intensive system design. The main engineering principle behind it is that the inherent complexity of software development can only be mastered by building, analyzing and manipulating system models. MDA also deals with system complexity by providing component-based design techniques, allowing independent component design, implementation and deployment, and then system integration and reconfiguration based on component interfaces. The model of a system in any stage is an integration of models of different viewpoints. Therefore, for a model-driven method to be applied effectively, it must provide a body of techniques and an integrated suite of tools for model construction, validation, and transformation. This requires a number of modeling notations for the specification of different concerns and viewpoints of the system. These notations should have formally defined syntaxes and a unified theory of semantics. The underlying theory of the method is needed to underpin the development of tools and correct use of tools in software development, as well as to formally verify and reason about properties of systems in mission-critical applications. The modeling notations, techniques, and tools must be designed so that they can be used seamlessly in supporting development activities and documentation of artifacts in software design processes. This article presents such a method, called the rCOS, focusing on the models of a system at different stages in a software development process, their semantic integration, and how they are constructed, analyzed, transformed, validated, and verified.     

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.     

面向对象的农产量预测专家系统设计与实现

本文介绍了用面向对象方法建造农产量预测专家系统的过程。提出了知识表示与推理机制不可分割，并且作了论述，证明向对象方法适合建造专家系统。在此基础上，讨论了面向对象的知识获取方法及原型知识表示，较为形式化地阐明了推理机制，最后讨论了基于Ｃ＋＋的实现技术。     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.