Application of a regionalized knowledge-based model for classifying the impacts of nitrogen,sulfur, and organic acids on lakewater chemistry

To maintain healthy ecosystems, it is increasingly imperative that federal land managers be prepared to monitor and assess levels of atmospheric pollutants and ecological effects in national parks, wildlife refuges, and wilderness areas. Atmospheric deposition of sulfur and/or nitrogen has the potential to damage sensitive terrestrial, and especially aquatic, ecosystems and can affect the survival of in-lake and in-stream biota. Federal land managers have a need to assess, at the individual park or wilderness area level, whether surface water resources are sensitive to air pollution degradation and the extent to which they have been impacted by atmospheric deposition of sulfur or nitrogen or influenced by other complicating factors. The latter can include geologic sources of sulfur, natural organic acidity, and the influence of disturbance and land use on water quality. This paper describes a knowledge-based decision support system (DSS) network for classifying lakewater resources in five acid-sensitive regions of the United States. The DSS allows federal land managers to conduct a preliminary assessment of the status of individual lakes prior to consulting an acid–base chemistry expert. The DSS accurately portrays the decision structure and assessment outcomes of domain experts while capturing interregional differences in acidification sensitivity and historic acid deposition loadings. It is internally consistent and robust with respect to missing water chemistry input data.     

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.     

Automated Integration of External Databases: A Knowledge-Based Approach to Enhancing Rule-Based Expert Systems

Expert system applications in the biomedical domain have long been hampered by the difficulty inherent in maintaining and extending large knowledge bases. We have developed a knowledge-based method for automatically augmenting such knowledge bases. The method consists of automatically integrating data contained in commercially available, external, online databases with data contained in an expert system's knowledge base. We have built a prototype system, named DBX, using this technique to augment an expert system's knowledge base as a decision support aid and as a bibliographic retrieval tool. In this paper, we describe this prototype system in detail, illustrate its use, and discuss the lessons we have learned in its implementation.     

Hardware-Software Partitioning at the Knowledge Level

Hardware-software co-design addresses the development of complex heterogeneous systems looking for the best tradeoffs among the different solutions. The basic idea is to combine the hardware and software design cycles. This article shows how knowledge-based techniques can be used to solve the hardware-software partitioning problem, the co-design task that makes the decision on the best implementation of the different components of a digital system. In particular, a fuzzy-logic-based expert system, SHAPES, has been developed based on the CommonKADS methodology. This tool takes advantage of two important artificial intelligence bases: the use of an expert's knowledge in the decision-making process and the possibility of dealing with imprecise and usually uncertain values by the definition of fuzzy magnitudes.Expert system construction has adopted a knowledge modeling approach, following the knowledge level and knowledge separation principles. This expertise model is the center of the knowledge-based system development. It is based in the problem-solving method Propose and Revise with a previous heuristic classification.     

利用专家系统建模——对人工智能的应用研究

专家系统是为了方便对各种问题制定决策,模拟专家的推理而设计的计算机程序。专家系统在表现那些需要制定决策的问题解决任务中尤其有效。建构专家系统规则库的学生对任一知识领域内概念之间动态的、临时的关系都会进行反思性思考。建构专家系统用到的思维方式可能是各种认知工具中最难的,因为它需要形式推理与逻辑推理,建构专家系统需要智力上的参与和挑战。     

An easy-maintenance,reusable approach for building knowledge-based systems: application to landscape assessment

Multiple Classification Ripple-Down Rules (MCRDR) is an extended methodology which allows an expert to build and maintain a knowledge-based system for multiple classification without technical assistance. Current MCRDR knowledge bases do not exhibit an explicit model of the relationships for the domain terms used by the expert. This is a strong impediment for both reusing and sharing of MCRDR knowledge bases, as well as for rapid development and maintenance. In this work, we describe how a domain knowledge ontological framework can be integrated with MCRDR, so providing this with explicit reusable knowledge components.     

ESR—A large knowledge-based system project of european power generation industry

The paper presents an integrated knowledge-based system aimed at providing technical support to engineers performing assessment and management of remaining life of the high-temperature pressurized components, mainly piping, in power plants. The system (ESR) is being developed at MPA Stuttgart (Germany) with support and under sponsorship of seven European electric utility companies. The initial requirements imposed by the utilities have been met by integrating various sources of knowledge, information, and data, as well as various information technology tools and techniques. The expert system modules use both production rules and object-oriented programming (decision/analysis support) and rely strongly on other integrated software: hypermedia (documentation base and explanation facility), numerics (engineering calculi), and data bases (material, standard geometries, etc.).     

Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.     

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.     

A decision-making module for aiding ship system automation design: A knowledge-based approach

The use of elements of artificial intelligence, including knowledge-based systems, becomes more and more widespread in aiding design problem solutions. The authors have been working on problems of control systems for many years. A design process involves many decision problems connected with, for example, a choice of a subsystem structure, subunits or particular elements selection. Because of such regards, it was decided to extend knowledge-based system with a module for support of such decision making.In this paper, an elaborated module for decision-making support is considered. The basic theoretical assumptions concerning the accepted method of multiattribute decision making based on pairwise comparison in categories of hierarchical decision process (AHP) is presented. Accepted knowledge representation in AHP method and pairwise comparison method and methods of expert knowledge acquisition are discussed. The module functioning is illustrated by an example of choice of temperature sensors in a system of fuel transport to Diesel engine of a main propulsion unit of a ship.     

Decopan Design: a knowledge-based system for industrial controlgear panels design

A wide domain for expert systems application is that of design processes. Decopan Design is a knowledge-based system supporting the design of industrial controlgear panels. Such a design process requires declarative knowledge, heuristic knowledge and human experience and skill, which are excellent subjects in order to be incorporated in a knowledge-based expert system. Decopan Design has been created using an expert system shell and has been linked with a computer-aided drafting program for drawing purposes.     

Maintenance tools for knowledge-based systems: the MAKE project

One of the major obstacles to the routine exploitation of knowledge-based and expert systems, is the difficulty of validating the knowledge base, and of maintaining it in a state which reflects current knowledge. This is of particular importance for systems based on law or regulations, where it is vital that the knowledge base be a true reflection of the legal position, and where there is a constant stream of changes to the correct legal position. Maintenance Assistance for Knowledge Engineers (MAKE) is a project designed to explore these issues, and to build a set of tools which will support the validation and maintenance of knowledge bases deriving from regulations. These tools include facilities to examine the structural features of the knowledge base, so as to guard against redundancy, nonprovability and contradiction; facilities to identify parts of the knowledge base jeopardised by changes in the domain, or in the understanding of the domain; and facilities to perform a variety of “house keeping” tasks. The paper firstly analyses the different types of change that may be required to maintain the knowledge base, and then proceeds to describe the set of tools developed in the MAKE project to accomodate these changes.     

On the limits of expert systems and engineering models in process control

The review is based on an analysis of current literature of expert systems and of system engineering models in dynamic process control. It starts with an analysis of the mental operations and cognitive requirements needed for supervisory control. Mental models are discussed as a function of situational requirements as well as of personal strategies. Systems engineering models and expert systems are briefly described and their function as decision support tools evaluated. Criteria are the overall functionality, similarity of knowledge bases and reasoning strategies of the human and the support system, adaptability to the operator's skill level and self-explanation of the support system in the interaction mode. As a result, system engineering models are only of limited value for knowledge-based process control. Expert systems seem to be very valuable tools for augmenting human decision making in process control, if the interaction problem can be solved.     

Knowledge engineering concepts for decision support system design and implementation

The design of computer-based systems that simulate expert human consulting by drawing on large amounts of task-specific knowledge has been a major research activity of applied artificial intelligence over the last ten years. Building decision support systems that incorporate aspects of this research is a promising new field. The purpose of this paper is to discuss concepts of “knowledge engineering” that are most relevant in designing and building knowledge-based decision support systems.     

Paradigms for knowledge-based systems in industrial engineering

When developing expert systems, expertise lies not only in formulating the knowledge to be put into the knowledge base, but also in deciding upon the knowledge representation and inference mechanism most suited to the application. Six detailed knowledge bases demonstrate the application of various AI-based systems to industrial engineering problems. They illustrate a number of approaches: expert systems, which are based upon practical experience; decision systems, which derive from modelling skills; and situation-action systems, which rely on production process design skills. The six paradigms presented describe a logical expert system for selecting material handling equipment; a multi-valued expert system for selecting a dispatching rule for automatic guided vehicles; a profile matching expert system for selecting project management software; a confidence building expert system for selecting a machine feeder; a tandem decision system for developing a production schedule; and a situation-action system for controlling job allocation in a flexible manufacturing cell. The relationships between these various paradigms and the characteristics of problems to which they can be applied are categorized by the nature of the expert and his expertise; the features of the environment; the decision or decisions to be taken; and the manner in which AI-system performance can be evaluated. A knowledge base is proposed for determining which architecture is most appropriate for a given application.     

Dynamic evaluation of the development process of knowledge-based information systems

Codifying expert domain knowledge is a difficult and expensive task. To evaluate the quality of the outcome, often the same domain expert or a colleague of similar expertise is relied on to undertake a direct evaluation of the knowledge-based system or indirectly by preparing appropriate test data. During an incremental knowledge acquisition process, a data stream is available, and the knowledge base is observed and amended by an expert each time it produces an error. Using the kept record of the system’s performance, we propose an evaluation process to estimate its effectiveness as it gets evolved. We instantiate this process for an incremental knowledge acquisition methodology, Ripple Down Rules. We estimate the added value in each knowledge base update. Using these values, the decision makers in the organisation employing the knowledge-based information system can apply a cost-benefit analysis of the continuation of the incremental knowledge acquisition process. They can then determine when this process, involving keeping an expert online, should be terminated. As a result, the expert is not kept on-line longer than it is absolutely necessary. Hence, a major expense in deploying the information system—the cost of keeping a domain expert on-line—is reduced.     

Eliciting knowledge and transferring it effectively to aknowledge-based system

The knowledge acquisition bottleneck impeding the development of expert systems is being alleviated by the development of computer-based knowledge acquisition tools. These work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system. However, the elicitation of expert knowledge and its effective transfer to a useful knowledge-based system is complex and involves diverse activities. The complete development of a decision support system using knowledge acquisition tools is illustrated. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering     

The role of domain knowledge in software design

Abstract

If effective knowledge-based support is to be provided for software designers, the process of software design, and the classes of knowledge used by designers must be understood more clearly. It has been shown that a software designer's experience of designing software in the current application domain has a significant effect on the production of a quality design. However, in gaining experience of designing software, a designer gains knowledge in various distinct areas, including software design and the application domain. It is currently unclear which elements of this experience are important. In particular, the role of application domain knowledge that is independent of software design is of great significance for builders of intelligent software design support systems, since the overheads involved in providing application domain knowledge for a variety of application domains in such systems would be huge. This paper reports on a study that has been carried out to gain insights into this question, based around the structured techniques of DeMarco (1979) and Yourdon and Constantine (1979). From this preliminary investigation it would appear that a designer's general knowledge of the application domain does not affect the quality of a design produced for a system in this domain; this runs contrary to current popular beliefs.     

DAISY: a decision support design methodology for complex,experience-centered domains

Users at different levels of domain experience have very different needs. For example, a system designed to assist domain novices may frustrate experts and vice-versa. This is one of several challenges specific to building decision support systems for experience-centered domains. A second challenge in working with complex experience-centered domains is that it is hard for non-experts to understand the domain in order to model it. In this paper we present DAISY, the design aid for intelligent support systems. It is a software design methodology for constructing decision support systems in complex, experience-based domains. DAISY address the specialized challenges of these domains by augmenting existing cognitive engineering methodologies. In particular, DAISY provides a method for identifying the specialized needs of users within a specific range of domain experience. Thus, it can help software designers to understand "What does the domain expert need?" or "What does a trained novice need?" To help system designers manage the complexity of modeling unfamiliar experience-centered domains, it provides a tool called a time/activity matrix. To illustrate each of DAISY's steps, we used the development of a decision support system called Fox. Fox assists expert military planners by rapidly generating alternative plans. This is a cognitively difficult, time critical task with life and death consequences     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.