Michel Manago

Diagnosis and Decision Support

In this chapter, we will focus on the utilization of Case-Based Reasoning for solving problems in the area of diagnosis and decision support. For this, we will first discuss different types of analytic problem solving, explain alternative approaches of coping with specific problems, and finally sketch a number of successful applications.

Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations

We propose in this paper a general framework for integrating inductive and case-based reasoning (CBR) techniques for diagnosis tasks. We present a set of practical integrated approaches realised between the Kate-Induction decision tree builder and the Patdex case-based reasoning system. The integration is based on the deep understanding about the weak and strong points of each technology. This theoretical knowledge permits to specify the structural possibilities of a sound integration between the relevant components of each approach. We define different levels of integration called “cooperative”, “workbench” and “seamless”. They realise respectively a tight, medium and strong link between both techniques. Experimental results show the appropriateness of these integrated approaches for the treatment of noisy or unknown data.

INRECA: A Seamlessly Integrated System Based on Inductive Inference and Case-Based Reasoning

This paper focuses on integrating inductive inference and case-based reasoning. We study integration along two dimensions: Integration of case-based methods with methods based on general domain knowledge, and integration of problem solving and incremental learning from experience. In the Inreca system, we perform case-based reasoning as well as tdidt (Top-Down Induction of Decision Trees) classification by using the same data structure called the Inreca tree. We extract decision knowledge using a tdidt algorithm to improve both the similarity assessment by determining optimal weights, and the speed of the overall system by inductive learning. The integrated system we implemented evolves smoothly along application development time from a pure case-based reasoning approach, where each particular case is a piece of knowledge, to a more inductive approach where some subsets of the cases are generalised into abstract knowledge. Our proposed approach is driven by the needs of a concrete pre-commercial system and real diagnostic applications. We evaluate the system on a database of insurance risk for cars and an application involving forestry management in Ireland.

Collecting Experience on the Systematic Development of CBR Applications Using the INRECA Methodology

This paper presents an overview of the INRECA methodology for building and maintaining CBR applications. This methodology supports the collection and reuse of experience on the systematic development of CBR applications. It is based on the experience factory and the software process modeling approach from software engineering. CBR development experience is documented using software process models and stored in different levels of generality in a three-layered experience base. Up to now, experience from 9 industrial projects enacted by all INRECA II partners has been collected.

Induction and Case-Based Reasoning for Classification Tasks

We present two techniques for reasoning from cases to solve classification tasks: Induction and case-based reasoning. We contrast the two technologies (that are often confused) and show how they complement each other. Based on this, we describe how they are integrated in one single platform for reasoning from cases: The Inreca system.

Using Information Technology to Solve Real World Problems

We present an induction algorithm, KATE, whose learning strategy is similar to the ID3 algorithm but which can handle examples described by several object, relations between objects, and use background domain knowledge to constrain the search space. The efficient numeric learning techniques used in ID3 have been combined with a rich symbolic knowledge representation language (frames) which allows using known induction techniques for a broader range of applications.

Acquiring Descriptive Knowledge for Classification and Identification

During the past decade, numerous real world knowledge-based systems have been built for the purpose of identification. Although most identification systems are based on the ability to observe and describe, few systems adress one of the first steps in knowledge acquisition which is how to acquire descriptions. Collecting this descriptive knowledge (observed facts) requires that a descriptive model (observable facts) has been previously defined. In addition, experience shows that the model depends on the goal which is pursued. In this paper, we present a tool and a methodology for the acquisition of the descriptive knowledge and the corresponding model which was designed primarily for identification. To achieve this goal, we have first used induction and have ran into redhibitory problems due to some limitations of this technology for processing incomplete descriptions. We present how we have stretched the technology in a case-based reasoning fashion to overcome these limitations. The tools and methodology have been developped and validated in the context of several real world applications.

Selected Applications of the Structural Case-Based Reasoning Approach

In this chapter we describe four applications that have been developed using the structural CBR approach and the INRECA methodology: The case-based help-desk support system HOMER, the Analog Devices’ product catalog, the case-based maintenance application for the high-speed train TGV, and the Fraunhofer IESE experience factory. The four systems represent common application areas for CBR techniques. They are distinct in terms of their complexity, the representation and similarity measures that are needed to develop the systems, and the processes that have to be put into place to operate the applications. Hence, they illustrate the versatility of the INRECA approach and the bandwidth of solutions that can be developed with it.

Development of Industrial Knowledge Management Applications with Case-Based Reasoning

The successful development, deployment and utilization of Case-Based Reasoning Systems in commercial environments require the development team to focus on aspects that go beyond the core CBR engine itself. Characteristics of the Users, the Organization and the Domain have considerable impact on the design decisions during implementation and on the success of the project after deployment. If the system is not technically and organizationally integrated with the operating environment, it will eventually fail. In this chapter, we describe our experiences and the steps we found useful while implementing CBR applications for commercial use. We learned these lessons the hard way. Our goal is to document our experience and help practitioners develop their own approach and avoid making the same mistakes.

About this Author

With this book you can learn a proven method for solving business problems. And you’ll come to understand why this methodology is the right solution for you. It addresses issues that are important for both software managers and technical staff. These issues will be particularly useful before the start or the set-up of an investment in building a case-based application.