Similarity, Uncertainty and Case-Based Reasoning in PADTEX

PATDEX is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the MOLTKE workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, MOLTKE contains other parts as well, in particular a model-based approach; in PATDEX where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe PATDEX from a principal point of view and embed its main concepts into a theoretical framewor

Reutilization of diagnostic cases by adaptation of knowledge models.

International audienceThis paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE)

Sistemas híbridos neuro-simbólicos: una revisión.

Este artículo presenta una revisión general de los sistemas híbridos neuro-simbólicos de inteligencia artificial, centrándose en aquellos compuestos por Sistemas de Razonamiento Basados en Casos (CBR) y Redes Neuronales Artificiales (ANN). Un sistema híbrido de inteligencia artificial está formado por la integración de varios subsistemas inteligentes, los cuales colaboran entre sí y se influyen mutuamente. En este artículo se muestran varias clasificaciones de estos sistemas, prestando especial atención a las características distintivas de cada uno de los subsistemas que componen los modelos híbridos

Adaptation of Cases for Case Based Forecasting with Neural Network Support

A novel approach to the combination of a case based reasoning system and an artificial neural network is presented in which the neural network is integrated within the case based reasoning cycle so that its generalizing ability may be harnessed to provide improved case adaptation performance. The ensuing hybrid system has been applied to the task of oceanographic forecasting in a real-time environment and has produced very promising results. After presenting classifications of hybrid artificial intelligence problem-solving methods, the particular combination of case based reasoning and neural networks, as a problem-solving strategy, is discussed in greater depth. The hybrid artificial intelligence forecasting model is then explained and the experimental results obtained from trials at sea are outlined

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

A case-based reasoning diagnosis system for AHU (Air-Handling Unit)

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal