A CBR Shell to Implement and Test Case Based Diagnostic Models

This paper analyses the eff icacy of Case Based reasoning models; more in particular the capabilit y to store and retrieve free stile types of information and data relative to expert knowledge domains is evaluated. Generall y Knowledge Based Systems are based on qualitative models of the expert knowledge stored in form of objects and rules that are managed by a predefined rule’s firing strategy. In this research field many prototype systems was developed, but often they failed to be applied for real applications due to rigidity and lack of flexibilit y to manage the uncertainty of the real data and information. To test and analyse the novel Case Base Reasoning mechanism it was developed a software shell based on the Case Based model described inside the paper. 1 INTRODUCTION Case Based Reasoning approach starts from hypothesis that is not feasible the attempt to replicate intelli gence by means of procedural and logical reasoning methods. It is based on the assumption that an algorithmic procedure can only exhibit a closed sets of behaviours that have been foreseen by the procedure’s designer (Stringa 1994), while a real intelli gent system, operating in a real world domain, must be able to handle unforeseen situations and/or situations that are more or less different by well known ones. A Case Base is a special type of Data Base containing all the most characteristic behaviours or working features (Wettschereck and Aha 1995) of a certain mechanism or physical system. It is not an unstructured collection of information items, but is devoted to solve defined problems inside the physical system and to support the execution of operative goals and activities. Inside the Case Base a single Case is a feature characterised by a certain set of feature attributes that may be more or less important to characterise the feature itself. The different attributes may be of boolean type (yes/no), of list type (A, B, C, ect.), of numerical type (temperature, pressure values etc). Two cases are more or less similar if they have a more or less similarity (or vicinity) between the respective attributes (Ricci and Avesani 1995)( Lowe 1995). The main CBR cycle is represented in fig. 1 by the so called four “RE” as described in (Aamodt and Plaza 1994): Retrieve, that means to search in the Case Base a problem solution, controlli ng what Cases in the Case Base are more similar to the current Case; Reuses the retrieved Cases to solve the proposed problem and define a solution; Revise the defined solution if necessary; Retain in the Case Base the new solution. Fig 1 A typical CBR working cycle One of the principal goal of this work is to present a method, based on well defined mathematical formalisations, to increase the eff iciency of the Cases retrieving and revising process applied to industrial diagnostic problems. The retrieving process consists in continuous evaluation of the distance between the current Case and the stored Cases while the current Case attributes will be solved; in a diagnostic process this is realised by a continuous answering to the diagnostic questions representing the symptoms of the problem. To solve in a reasonable time the diagnostic problem it is very important to propose the questions to be answered in terms of their suitabilit y to produce a faster problem solution. In other word, the different questions that compose the attribute quantity space of the Case Base, must be ordered taking in account their importance to solve the problem and their costs in terms of diff iculty to furnish an answer to the question itself. In the more classical rules based systems, containing a quali tative model of the diagnostic problem, the task to increase the process eff iciency is carried out by the so called meta-rules containing the meta-knowledge of the proposed problem. In fact, the way to determine the lower or higher importance degree of a certain question for a diagnostic problem solution, is one of the most important types of knowledge of a human expert (strategic knowledge). One of the objectives of this paper is also to define a method to evaluate the question’s importance directly from the Case Base without the expert support. 2 THE CASE BASED REASONING MODEL The objective of the CBR model is to evaluate the vicinities between the archived Cases and a current new Case. The evaluation of vicinity between Cases is performed allocating all Cases in the appropriate points inside a multi -dimensional qualitative space and using an appropriate metric to determine distances between their attribute’s values. 2.1 The Questions quali tative space A question set Dk = { dm1 , . , dmn } is a set of objects every one representing an attribute of a certain problem or Case K. If n is the total number of questions for a certain Case k, Dk is an multi-dimensional space called question space. Problem Retrieve