Literature has, however, indicated that Case Based Reasoning (CBR) is a suitable technique for use in the interpretation of UNDT data. This paper supports the ...
IADIS International Conference Applied Computing 2007
DEFINING CASE BASED REASONING CASES WITH XML E. Asemota*†, S. Gallagher†, G. McRobbie† and S. Cochran‡ MicroScale Sensors, School of Computing† and School of Engineering and Science‡, University of Paisley, Paisley, PA1 2BE, UK
ABSTRACT The interpretation of ultrasonic non-destructive destructive testing (UNDT) data is, largely, a human-centric activity which requires a significant level of expertise. As such, it is difficult to clearly define the rules used for interpreting this data. Many computational techniques have been both proposed and deployed to assist in the interpretation of this data: from procedural language developments through to advanced Artificial Intelligence-based solutions involving Expert Systems and Artificial Neural Networks. However, literature indicates that due to the ill-defined nature of the interpretation of UNDT data, solutions developed using the aforementioned techniques are often considered as being less than complete and robust solutions. Literature has, however, indicated that Case Based Reasoning (CBR) is a suitable technique for use in the interpretation of UNDT data. This paper supports the notion of using CBR in this domain with one caveat. That is, that within any such CBR solution for this domain, the use of rigid case definition is inappropriate. Rigid case definitions make it more likely that some degree of redevelopment of the CBR system will be required when a case definition changes. As it is expected that case definition will change in an ill-defined domain such as the interpretation of UNDT data, an alternative approach that manages such changes in case definition, is required. Such an approach would be able to handle the CBR case changes without necessitating any degree of system redevelopment, i.e. is one which is both flexible and configurable at runtime. This paper outlines the development of a run-time configurable case definition technique which enables a flexible case definition. The paper presents the pragmatic implementation of an XML-based, run-time configurable case definition solution. The body of work presented herein forms part of an overall program of research for the automatic interpretation of UNDT data. KEYWORDS CBR, XML, Ultrasonic NDT, ill-define domain
1. INTRODUCTION Once designed and manufactured, many engineering systems require periodic, in-situ, monitoring; reasons for this include optimum performance and safety. To assist this monitoring, non-destructive testing (NDT) techniques are often employed of which UNDT is one such technique (Pyzdek et al, 2003; Jayakumar and Thavasimuthu, 2002; Achenbach, 1999). UNDT is both an efficient and an effective technique, as it permits the examination of the internal and surface properties these engineering systems. In order to achieve this, UNDT transmits generated ultrasonic energy via an ultrasonic probe into the system under investigation. The technique yields significant volumes of data, which requires a high-level of expertise and, often, many manhours for its interpretation. Currently in industry, such interpretations are primarily performed by human inspectors (Ma et al, 2002; Simpson and Blitz, 1996). However, as literature identifies, such “human-centric” interpretations are often error prone and as these interpretation often determine the safety of such systems, there is a need to develop alternative approaches for this interpretation (Alexiev and Mihovski, 2000; Murgatroyd et al, 1990; Smith, 1998). Although the application of computer-based technique presents an alternative approach to the data interpretation, the complex nature of the data concerned and the lack of clearly defined interpretation rules makes it difficult to apply solutions such as expert systems within this domain (Jarmulak, 2001). Similarly, if an artificial neural network is utilised, the number of patterns such a system will be required to recognise will be impractically large (McNabb and Cornwell, 1995). Hence, case based reasoning (CBR) has been proposed
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as an appropriate computer-based technique to be employed in the computer-based interpretation of UNDT data as it does not require interpretation rules or data pattern for its training (Jarmulak, 2001). In addition, a CBR system is able to “learn” through the addition of new cases and the removal “irrelevant” (no longer relevant or appropriate) cases from the case library (Watson, 1997). Paradoxically, the lack of clearly defined rules for interpreting UNDT data (Drury, 1978) means that developing a CBR system for this domain is considered a difficult task with respect to the design of the cases. Traditionally, a CBR case defines all the features which the CBR system will utilise during its operations (retrieval, matching and adaptation) (Shiu and Pal, 2004; Riesbeck and Schank, 1989). As such, inaccurately designed cases may adversely affect the operation and performance of the entire system (Watson, 1997). However, as this domain (UNDT data interpretation) is ill-defined, the exact content of the contained cases is difficult to ascertain during the development process. As such, if the case definition changes after the system has been developed, the system may need to be redeveloped in order to support the new case definition. To mitigate these concerns, a run-time configurable case definition infrastructure has been implemented within this program of research. The implementation of this infrastructure is rooted in the use of extensible markup language (XML). As XML provides inbuilt structure and extensibility, other processes within the CBR operation can be abstracted to operate on the structure defined via the XML as opposed to the specifics of the features of a particular case. This paper presents the details of this infrastructure.
2. CASE BASED REASONING CBR problem resolution involves mapping current problems to previously encountered problems. To achieve this, a knowledge base (case library) which consists of a series of “cases” is employed. Each of these cases contains a “case-problem”, a “case-solution” and, optionally, a “case-outcome” and defines a specific experience within the domain which the CBR solution is designed to resolve. Within this experience, a “snap shot” of the problem is stored. That is, the relevant factors which define this experience (problem). Cases can be represented in a variety of formats. For example, they can be represented as simple text, video, etc appropriate to the specific domain. In this way, cases can be used to represent numerous experiences (Shiu and Pal, 2004; Kolodner, 1993; Schank, 1982; Hammond, 1988). The operation of a typical CBR system is depicted in Figure 1. Within this figure, an input problem is entered into the system. If “similarity” is established, the system then retrieves similar cases from the case library from which it reuses the best matching case’s solution as the solution of the input problem. The reuse of the retrieved best matching case’s solution is achieved through the case adaptation operation. If no similarity is established, then the input problem is categorised as a new case which may be added to the case library (Watson, 1997; Lebowitz, 1983; Kolodner, 1993; Koton, 1989; Leake, 1996; Hammond, 1988). The problem resolution capability of a CBR solution depends not only on the number of different cases stored within the case library but also on the content of the cases. The storage of cases which sufficiently describe the problem domain enhances the capability of the system (Watson, 1997).
Figure 1. High-level depiction of a typical CBR operation
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3. AN XML-BASED CBR CASE DEFINITION INFRASTRUCTURE Within the current programme of research, an XML-based run-time configurable CBR case definition infrastructure was developed. In this infrastructure, each case contains a “case-problem”, a “case-solution” and a “case-outcome” (see Section 3). The case-problem captures the inspection variables (material, probe and signal characteristics, the signal type, the probe operational frequency and the B-Scan image). These variables affect the pattern of the resultant data from an inspection (Drury, 1978). The solution applied to the case-problem is recorded via the case-solution. The case-solution is defined by a Boolean value (true or false) specifying whether the feature is a defect or not. The case-outcome records the outcome of an interpretation. The case-outcome is also a Boolean value with “true” indicating a successful interpretation and “false” indicating the converse. In the developed CBR system, cases stored in the case library are represented using Extensible Markup Language (XML) (Means and Harold, 2002; Marchal, 2001). Figure 2 shows the Markup for a typical, implemented case in the case base. Within this application, no case-solution can exist unless there is a caseproblem to which the case-solution is applied. Also, more than one case-problem can have the same casesolution. That is, two (or more) cases within the case library may contain case-problems which are, in themselves, different but have the same case-solutions. For example, these case-solutions may have the value of “true”.
Figure 2. A typical implemented case from the case library
Rather than store this solution twice, the physical storage of the cases is implemented in a manner that the three parts of a case (case-problem, case-solution and case-outcome) are stored in different data stores. Each case-problem has a “ProblemID” node which is a globally unique identifier (GUID) (Hamilton, 2003). Each case-solution and case-outcome also has a “ProblemID” node which records all case-problems that the specific case-solution or case-outcome applies to. The resultant effect of this implementation is that only one version of a particular case-solution is ever physically stored within the case library. Thus, the sizes of both the case-solution and case-outcome data stores are kept at minimum which in turn, enhances retrieval operations from these data stores. The above relationship between a case and its constituent parts is depicted
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in Figure 3. Within this diagram, each case comprises a case-problem which is associated with a casesolution and a case-outcome via the case-problem “ProblemID”.
Figure 3. Relationship between constituent parts of a case
The developed CBR application is part of an overall solution which, at its core, contains a controller module that monitors the various parts of the solution (Asemota et al., 2006). This controller generates the CBR input problem’s Markup (XML case definition) and transfers this problem to the CBR application. In order to generate this Markup, the controller accumulates data from all modules activated within the solution. However, the number of modules activated varies depending on the particular requirements of an inspection. In addition, the controller may interface with external systems and in such scenarios, the case definition must reflect the impact of these external systems on the UNDT data which the CBR application is designed to interpret. Thus, the need for this implemented flexible and configurable case definition structure. Figure 4 illustrates this overall solution indicating the modules contained therein and their interactions with the controller (framework controller).
Figure 4. High-level architecture of the overall research solution
4. CONCLUSION This paper has identified the fact that as UNDT data interpretation is both complex and ill-defined in nature, the application of CBR in this domain requires a flexible infrastructure for case definition. Within the current program of research, a CBR system which utilises XML to define cases in its case library was developed. As
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XML is both structured and extensible, the case definition infrastructure is able to manage any required changes to case definition at run-time without necessitating any redevelopment of the application. The developed infrastructure forms part of an implemented research solution for the automatic interpretation of UNDT data which is able to integrate external systems. The flexibility offered by this implemented XML-based case structure allows the framework to handle data from these external systems without requiring any redevelopment of the CBR-Based application.
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