Stefan Wess

Case-Based Reasoning Technology

Extending some Concepts of CBR - Foundations of Case Retrieval Nets.- Diagnosis and Decision Support.- Intelligent Sales Support with CBR.- Textual CBR.- Using Configuration Techniques for Adaptation.- CBR Applied to Planning.- CBR for Design.- CBR for Experimental Software Engineering.- CBR for Tutoring and Help Systems.- CBR in Medicine.- Methodology for Building CBR Applications.- Related Areas.

Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning

Retrieval of cases is one important step within the case-based reasoning paradigm. We propose an improvement of this stage in the process model for finding most similar cases with an average effort of O[log2n], n number of cases. The basic idea of the algorithm is to use the heterogeneity of the search space for a density-based structuring and to employ this precomputed structure, a k- d tree, for efficient case retrieval according to a given similarity measure. Besides illustrating the basic idea, we present empirical results of a comparison of four different k- d tree generating strategies and introduce the notion of dynamic bounds which significantly reduce the retrieval effort. The presented approach is fully implemented and used within two case-based reasoning systems for classification and diagnostic tasks, Patdex and Inreca.

Similarity, Uncertainty and Case-Based Reasoning in Patdex

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 framework.

Intelligent Sales Support with CBR

Electronic commerce applications are just about to leave their infancy. While electronic cash has been around for quite a few years, the amount of business carried out through the Internet is still relatively small compared to the potential of this young technology. There are plenty of reasons for this. Tennenbaum summarizes the barriers for using this medium today with the three words: confidence, convenience, and content. Customers must have confidence that their transactions are secure, their privacy is maintained, and they will not be subject to liability. It must be convenient, as simple to use as ATMs and as ubiquitous. Finally, there must be incentives to purchase goods via the Internet, be they a better price, service, or selection.

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.

The Development of HOMER: A Case-Based CAD/CAM Help-Desk Support Tool

The increasing number of hardware and software at Daimler-Benz personal car development in Sindelfingen combined with the constant number of help-desk operators demanded a help-desk system which goes beyond the classical trouble-ticket approach. In this application paper we give an overview of the situation at the CAD/CAM Help-Desk in Sindelfingen and the development of the case-based help-desk support tool HOMER. We describe our modeling approach and its influence on the system architecture as well as the different user roles and the help-desk tool itself. We conclude with the lessons learned during the course of this project and future prospects.

Goal-Driven Similarity Assessment

While most approaches to similarity assessment are oblivious of knowledge and goals, there is ample evidence that these elements of problem solving play an important role in similarity judgements. This paper is concerned with an approach for integrating assessment of similarity into a framework of problem solving that embodies central notions of problem solving like goals, knowledge and learning. We review empirical findings that unravel characteristics of similarity assessment most of which have not been covered by purely syntactic models of similarity. A formal account of similarity assessment that allows for the integration of central ideas of problem solving is developed. Given a goal and a domain theory, an appropriate perspective is taken that brings into focus only goal-relevant features of a problem description as input to similarity assessment.

Case-based and symbolic classification

Contrary to symbolic learning approaches, that represent a learned concept explicitly, case-based approaches describe concepts implicitly by a pair (CB,sim), i.e. by a measure of similarity sim and a set CB of cases. This poses the question if there are any differences concerning the learning power of the two approaches. In this article we will study the relationship between the case base, the measure of similarity, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the hypothesis of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.

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.