Designing knowledge-based systems within functional architecture: the DIVA experiment

Abstract

Solving a non-trivial problem can rarely be reduced to performing a single, simple task. Within any complex knowledge-based system, there exists a number of tasks to be performed to solve the problem. Making these tasks explicit has been a recurrent concem over the past few years. This has led to functional architecture for knowledge-based systems. The purpose of this paper is to assess the use of functional architecture for knowledge-based systems. The discussion will be based on the experience gained while designing DIVA, an expert system for vibration-based monitoring of large rotating machinery. We first describe the system and show how its task decomposition results from the characteristics of the problem. We then describe how this approach enables to build more sophisticated explanations of reasoning. We present how knowledge acquisition has been achieved in this framework. And lastly, we discuss the maintainability of such systems.Status: field-test Domain: vibration-based diagnosis of rotating machinery Language: DIVA is written in LORE [4], an object-oriented language developed at Laboratoires de Marcoussis. LORE is written in Sun Common Lisp.Effort: 12 person-years to date. ABSTRACT Solving a non-tivial problem can rarely be reduced to performing a single, simple task. Within any complex knowledge-based system, there exists a number of tasks to be performed to solve the problem. Making these tasks explicit has been a recurrent concem over the past few years. This has led to functional architecture for knowledge-based systems.The purpose of this paper is to assess the use of functional architecture for knowledge-based systems. The discussion will be based on the experience gained while designing DIVA, an expert system for vibration-based monitoring of large rotating machinery. We first describe the system and show how its task decomposition results from the characteristics of the problem. We then describe how this approach enables to build more sophisticated explanations of reasoning. We present how knowledge acquisition has been achieved in this framework. And lastly, we discuss the maintainability of such systems. I74

A systematic review and taxonomy of explanations in decision support and recommender systems

“…Approach [166,34,56,206,2,124,54,146,198,192,129,131,86,138,82,136,24,97,4,171,249,84,231,94,101,143,252,149,85,208,77,189,220,68,64,6,52,29,167,230,23,96,47,242,117,254,15,46,9,156,44,236,88,…”