Mapping A Knowledge Level Analysis Onto A Computational Framework

Mapping a Knowledge Level Analysis Onto a Computational Framework

KADS

KADS is a structured methodology for the development of knowledge based systems which has been adopted throughout the world by academic and industrial professionals alike. KADS approaches development as a modeling activity. Two key characteristics of KADS are the use of multiple models to cope with the complexity of knowledge engineering and the use of knowledge-level descriptions as an immediate model between system design and expertise data. The result is that KADS enables effective KBS construction by building a computational model of desired behavior for a particular problem domain. KADS contains three section: the Theoretical Basis of KADS, Languages and Tools, and Applications. Together they form a comprehensive sourcebook of the how and why of the KADS methodology. KADS will be required reading for all academic and industrial professionals concerned with building knowledge-based systems. It will also be a valuable source for students of knowledge acquisition and KBS. * SPECIAL FEATURES: * KADS is the most widely used commercial structured methodology for KBS development in Europe and is becoming one of the few significant AI exports to the US. * Describes KADS from its Theoretical Basis, through Language and Tool Developments, to real Applications.

The Knowledge Level in Expert Systems

The Knowledge Level In Expert Systems: Conversations and Commentary deals with artificial intelligence, cognitive science, qualitative models, problem solving architectures, construction of knowledge bases, machine learning integration, knowledge sharing or reusability, and mapping problem-solving methods. The book tackles two opposing dogmas: first, that control is generic so is in the inference engine; and two, deep and surface knowledge are different so deep knowledge belongs in a performance system. The text also explains how to use SPARK, a selection method, in approaching the task features that can be used to select or construct the problem-solving method suitable for the task. An alternative method to SPARK starts with an analysis of the domain model and a classification using primitive inference steps. The book also adds that expert problem solving is a form of qualitative modeling that connects other expert systems and engineering. The text then describes very large knowledge bases, particularly, the volume of which knowledge bases can be integrated with expert systems, coherence maintenance, and use/neutral representation of knowledge. Task analysis and method selection focuses on SPARK; how theories about the relation between task features and expert system solutions can be empirically validated. The book also enumerates the benefits and limitations of a generic task approach, and how various modules with their specific internal architectures can be integrated. Programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers such as programming, networking, engineering or design will find the book highly useful.