Acquiring Systems Knowledge with GOOI (Graphical Object-Oriented Interfaces)

Information system development, in particular expert systems and other knowledge-based approaches, require extensive human expert knowledge. Often, acquiring such knowledge is problematic with regard to efficiently acquiring the expert’s knowledge and translating this knowledge into a system usable form. Knowledge acquisition has long been viewed as the bottleneck of knowledge-based systems and more recently is being recognized as a significant issue in general information systems analysis and design. Object-oriented techniques are presented as a uniform method for overcoming translation difficulties and implementing graphical interfaces. A graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for our tool provides an additional benefit in developing implemented systems by providing a representation independent methodology that can easily be mapped into any other object-oriented based expert system or other object-oriented information systems

Designing Software Architectures As a Composition of Specializations of Knowledge Domains

This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience

Advanced technologies for Mission Control Centers

Advance technologies for Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: technology needs; current technology efforts at GSFC (human-machine interface development, object oriented software development, expert systems, knowledge-based software engineering environments, and high performance VLSI telemetry systems); and test beds

Integration of object-oriented knowledge representation with the CLIPS rule based system

The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules

Automated extraction of knowledge for model-based diagnostics

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools

Graphical deep knowledge representation in VODAK/VML object-oriented database

Applying Object-oriented concepts to the design of complex graphical interface has received great attention in the database and knowledge representation disciplines. Traditional CAD systems can not support efficient environments for design processes because they store information about all the objects for display purposes but do not store any knowledge for reasoning purposes. They are called knowledge poor . Graphical Deep Knowledge in Artificial Intelligence has been proven successful to represent knowledge about objects for display purposes as well as reasoning purposes. We introduwd the theory of Graphical Deep Knowledge into the object-oriented database s stem VML to design a Knowledge rich system which can support better graphical interface. We showed that the theory of Graphical Deep Knowledge can improve graphical interface design and functions as a flexible, and knowledgeable tool for design processes. Based on the presented theory, we developed a system called GDKRIVVOOD (Graphical Deep Knowledge Representation In Vodak/VML Object-Oriented Database) for circuit board design