Case-based reasoning is a technique recently developed to alleviate limitations of the rule-based expert systems. Instead of relying solely on rules, a case-based system maintains old cases in a case base. When a new problem is encountered, the system retrieves similar cases from the case base and constructs a solution to the new problem based on existing solutions. A key issue in case-based reasoning is how to index and retrieve similar cases. In this paper, we present a new approach that integrates fuzzy set concepts into the case indexing and retrieval process. This approach has a few advantages over existing methods. First, it allows numerical features to be converted into fuzzy terms to simplify the matching process. Second, it allows cases in different domains to be comparable. Finally, it allows greater flexibility in the retrieval of candidate cases.

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1113&context=pacis1993

Fuzzy indexing and retrieval in case-based systems

Case-Based Reasoning CBR is rapidly emerging as a rich and powerful artificial intelligence approach that can overcome some inherent weaknesses of traditional expert system technology. Several successful CBR systems have been developed in a variety of fields and CBR applications are beginning to emerge in accounting research and practice as well. As sophisticated and user-friendly CBR shells emerge, CBR is more likely to be used to solve complex, real-world problems. This paper provides a broad overview of case-based systems and the principles on which the field is founded. Accounting applications of case-based reasoning and future research issues are identified.

Applying Case-Based Reasoning to the Accounting Domain

A study on the web-based intelligent self-diagnosis medical system

In this chapter, we propose a framework for integrated design, advertisement and retailing of garments over the Internet. This on-line shopping framework would make use of the latest research in computer graphics, image processing, computer vision and artificial intelligence. We describe these technologies in more detail, and explain how they can be used to build a visually attractive and easy to use interface to an intelligent integrated system that fulfils most of the functions of the traditional production chain, while allowing for mass-customization.

A Framework for Garment Shopping over the Internet

As part of the process of certification and commissioning of a new guided or automated rail transport system, the domain experts and in particular the National Safety Authority are responsible for reviewing the safety of the system to ensure that the safety level of the new transport system is at least equivalent to the railway systems already in service and deemed safe. This critical task of evaluating safety essentially concerns all the safety files prepared by the manufacturer and in particular safety studies such as the Preliminary Hazard Analysis (PHA), the functional safety analysis (FSA), the analysis of failure modes, their effects and of their criticality (AFMEC) or Software Error Effect Analysis (SEEA). The study presented in this paper is part of the SEEA analysis. To respect the completeness and consistency of this safety analysis (SEEA), the experts carry out complementary analyses of safety. They are brought to imagine new scenarios of potential accidents to perfect the exhaustiveness of the safety studies. In this process, one of the difficulties then consists in finding the abnormal scenarios being able to lead to a particular potential accident. This is the fundamental point that motivated this work. To help experts in this complex process of evaluating safety studies, we agreed to use artificial intelligence techniques and in particular machine learning to systematize, streamline and strengthen conventional approaches to safety analysis and critical software certification. The approach which was adopted in order to design and implement an assistance tool for safety analysis involved the following two main activities: – Extracting, formalizing and storing hazardous situations to produce a library of standard cases which covers the entire problem. This process entailed the use of knowledge acquisition techniques; – Exploiting the stored historical knowledge in order to develop safety analysis know-how which can assist experts to judge the thoroughness of the manufacturer’s suggested safety analysis. This second activity involves the use of machine learning techniques in particular the use of case-based reasoning (CBR).

Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport

Case-based reasoning (CBR), the hit of the American Association of Artificial Intelligence annual conference in 1991 and 1992 is now enjoying a surge of interest in its first year of commercial availability. Knowledge-based system designers, developers, integrators, and tool vendors are now seriously considering the role and utility of CBR in leveraging the vast experience within organizations for more effective decision making. The potential market for CBR appears enormous, particularly in more complex problem-solving domains, but the areas of most immediate interest are in applications where efficient information processing needs are urgent, such as automated help desks. Early experiments pairing CBR with rule-based systems will soon lead to hybrid combinations with other “close approximation” technologies, such as neural networks, fuzzy logic systems, genetic algorithms, and so forth. CBR appears headed for a sustaining role not only as a useful complement in knowledge-based information processing technology but also as an engine for “mainstream” information tasks of the future (e.g., intelligent text processing and retrieval, data mining, and projective reasoning). This article will discuss this emerging role for CBR and its implications from a marketing perspective.

Steve Mott

Papers

Case-based reasoning: Market, applications, and fit with other technologies