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.     

User studies of knowledge acquisition tools: methodology and lessons learned

Knowledge acquisition research concerned with the development of knowledge acquisition tools is in need of a methodological approach to evaluation. This paper describes experimental methodology to conduct studies and experiments of users modifying knowledge bases with knowledge acquisition tools. The paper also reports on the lessons learned from several experiments that have been performed using this methodology. The hope is that it will help others design user evaluations of knowledge acquisition tools. Ideas are discussed for improving the current methodology and some open issues that remain.     

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     

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.     

Automated Support for Building and Extending Expert Models

Building a knowledge-based system is like developing a scientific theory. Although a knowledge base does not constitute a theory of some natural phenomenon, it does represent a theory of how a class of professionals approaches an application task. As when scientists develop a natural theory, builders of expert systems first must formulate a model of the behavior that they wish to understand and then must corroborate and extend that model with the aid of specific examples. Thus there are two interrelated phases of knowledge-base construction: (1) model building and (2) model extension. Computer-based tools can assist developers with both phases of the knowledge-acquisition process. Workers in the area of knowledge acquisition have developed computer-based tools that emphasize either the building of new models or the extension of existing models. The PROTÉGÉ knowledge-acquisition system addresses these two activities individually and facilitates the construction of expert systems when the same general model can be applied to a variety of application tasks.     

CAPSUL: A constraint-based specification of repeating patterns in time-oriented data

We use a constraint-based language to specify repeating temporal patterns. The Constraint-based Pattern Specification Language (CAPSUL) is simple to use, but allows a wide variety of patterns to be expressed. This paper describes in detail the syntax of CAPSUL, including its layers of abstraction and four types of constraints. We also discuss the semantics of CAPSUL, including the concept of interference between patterns and the expressive power of the language. We have implemented CAPSUL in a temporal-abstraction system called Résumé, and used it in a graphical knowledge-acquisition tool to acquire domain-specific knowledge from experts about patterns to be found in large databases. We summarize the results of preliminary experiments using the pattern-specification and pattern-detection tools on data about patients who have cancer and have been seen at the Rush Presbyterian/St. Luke's Medical Center. This revised version was published online in June 2006 with corrections to the Cover Date.     

一种基于构件的企业信息系统开发支撑框架

如何设计、管理和利用构件快速、可靠地组装企业信息系统是软件开发工具研究的重要课题之一,该文介绍了“863”／CIMS国产数据库客户端开发工具OpenTools基于构件的软件开发支持框架,详细说明了其总体结构、构件库组织以及构件的分类,检索和集成机制。     

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.     

TOOLympics I: Competition on software testing

Research competitions and challenges are a driving force in transferring theoretical results into working software tools that demonstrate the state of the art in the respective field of research. Regular comparative evaluations provide guidance to practitioners that have to select new technology and tools for their development process. In order to support competitions and challenges with an appropriate publication venue, a new theme of issues in the International Journal on Software Tools for Technology Transfer was created. This issue is the inaugural issue of the newly introduced theme on “Competitions and Challenges” (CoCha). Test-Comp, the International Competition on Software Testing, is an example of a tool competition, where the research teams submit tools for test-generation, and the competition evaluates the tools and assigns scores according to achieved coverage. Test-Comp 2019 was part of the TOOLympics event, which took place as part of the 25-year celebration of the conference TACAS. Thus, it is most natural to start the new STTT-CoCha theme with a special issue that describes the results and participating systems of Test-Comp 2019. There will be a second issue on TOOLympics with contributions from other competitions.

     

Architecture and development environment of a knowledge-based monitor that facilitate incremental knowledge-base development

Being able to incrementally define and test knowledge bases for intelligent systems is desirable. However, as more knowledge is added, the knowledge engineer must ensure that unwanted interactions between the existing and additional knowledge do not occur. One knowledge-based monitoring system, Hazard Monitor (HM), provides the ability to add knowledge incrementally. HM's architecture includes tailorable components that allow the knowledge engineer to eliminate unwanted knowledge interactions. HM also includes knowledge-base development tools to facilitate initial and incremental knowledge-base development. This paper describes HM's architecture and knowledge structures plus its knowledge-base development tools that facilitate the knowledge engineering process. To illustrate the benefits of the architecture, we present aviation examples based on our implemented prototypes.     

工具接口技术研究

随着CASE环境（Computer Aided Software Engineering Environment)的不断发展，工具间如何协调高效地支持整个软件开发过程显得愈为重要，文中重点研究了CASE环境中工具间的接口技术，对各种类型的接口技术进行了分析和比较，并阐述了工具接口的应用问题。     

GDKAT: A goal-driven knowledge acquisition tool for knowledge base development

While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts' declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.     

Integrating knowledge acquisition and language acquisition

Very large knowledge bases constitute an important step for artificial intelligence and will have significant effects on the field of natural language processing. This paper describes LUKE, a tool that allows a knowledge base builder to create an English language interface by associating words and phrases with knowledge base entities. The philosophy behind LUKE is that knowledge about language is built up at the same time as knowledge about the world. LUKE assumes no linguistic expertise on the part of the user—that expertise is built directly into the tool itself. LUKE draws its power from a large set of heuristics about how words are typically used to describe the world.This research was supported in part by the National Science Foundation under contract IRI-8858085.     

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.     

DATA BASE MINING Discovering New Knowledge and Competitive Advantage

Buried in the huge data bases assembled by large organizations is information useful for generating new facts and relationships that can provide significant competitive advantage. This article describes how data base mining extracts knowledge from existing data bases, data base mining applications and their limitations, and bottom-line benefits.     

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.     

Case-based reasoning assisted explanation of genetic algorithm results

Abstract

This paper describes a system for explaining solutions generated by genetic algorithms (GAs) using tools developed for case-based reasoning (CBR). In addition, this work empirically supports the building block hypothesis (BBH) which states that genetic algorithms work by combining good sub-solutions called building blocks into complete solutions. Since the space of possible building blocks and their combinations is extremely large, solutions found by GAs are often opaque and cannot be easily explained. Ironically, much of the knowledge required to explain such solutions is implicit in the processing done by the GA. Our system extracts and processes historical information from the GA by using knowledge acquisition and analysis tools developed for case-based reasoning. If properly analysed, the resulting knowledge base can be used: to shed light on the nature of the search space; to explain how a solution evolved; to discover its building blocks; and to justify why it works. Such knowledge about the search space can be used to tune the GA in various ways. As well as being a useful explanatory tool for GA researchers, our system serves as an empirical test of the building block hypothesis. The fact that it works so well lends credence to the theory that GAs work by exploiting common genetic building blocks.     

Classification and selection of tools for quality knowledge management

Knowledge managers need to select which knowledge management tool to use for any given problem and problem environment. A graphical tool, named the “house of knowledge management tool selection” is proposed, based on the house of quality matrix used in the quality function deployment methodology. A simple case study is described that acts as a proof of concept to show the house of knowledge management Tool selection can systematically evaluate potential tools to solve a knowledge management problem. To help identify the tools to populate the house, an examination was undertaken of how knowledge management tools had previously been listed and classified, but these existing classifications were found to be of little help. No classification existed that categorised the tools in terms of the knowledge problems they helped resolve, yet this classification would seem more useful for knowledge managers. To meet this need, knowledge problems were divided into ten subtypes and the knowledge management tools were then categorised according to their effectiveness at solving each subtype. This new classification was flexible enough to include all types of knowledge management tools and could also change with each problem environment. It was found to give a greater understanding of the knowledge management tools in the context of a particular knowledge problem, and it could therefore help populate the house tool. The house of knowledge management tool selection is a promising development of a tool that should be able to become an essential part of a manager’s decision-making toolkit.