Béatrice Fuchs

Engineering and learning of adaptation knowledge in case-based reasoning

Case-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge. These various knowledge containers are characterised from the engineering and learning points of view. We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to model at the design stage. These difficulties motivate the use of a learning process for refining these knowledge containers. We argue that in an adaptation guided retrieval approach, similarity and adaptation knowledge containers must be mixed. We rely on a formalisation of adaptation for highlighting several knowledge units to be learnt, i.e. dependencies and influences between problem and solution descriptors. Finally, we propose a learning scenario called “active approach” where the user plays a central role for achieving the learning steps.

A Knowledge-Level Task Model of Adaption in Case-Based Reasoning

The adaptation step is central in case-based reasoning (CBR), because it conditions the obtaining of a solution to a problem. This step is difficult from the knowledge acquisition and engineering points of view. We propose a knowledge level analysis of the adaptation step in CBR using the reasoning task concept. Our proposal is based on the study of several CBR systems for complex applications which imply the adaptation task. Three of them are presented to illustrate our analysis. We sketch from this study a generic model of the adaptation process using the task concept. This model is in conformity with other CBR formal models.

Differential adaptation: an operational approach to adaptation for solving numerical problems with CBR

Case-based reasoning relies on four main steps: retrieval, adaptation, revision and retention. This article focuses on the adaptation step; we propose differential adaptation as an operational formalization of adaptation for numerical problems. The solution to a target problem is designed on the basis of relations existing between a source case (problem and solution) and a target case. Differential adaptation relies on the metaphor of differential calculus where small variations on variable values are related to variations of function values. Accordingly, variations between problems correspond to variations between variable values and variations between solutions to variations between function values. Operators inspired from differential calculus are able to manipulate the variations and to support the whole adaptation process. Differential adaptation is operational and provides generic operators that can be reused for different real-world numerical situations.

Towards a Unified Theory of Adaptation in Case-Based Reasoning

Case-based reasoning exploits memorized problem solving episodes, called cases, in order to solve a new problem. Adaptation is a complex and crucial step of case-based reasoning which is generally studied in the restricted framework of an application domain. This article proposes a first analysis of case adaptation independently from a specific application domain. It proposes to combine the retrieval and adaptation steps in a unique planning process that builds an ordered sequence of operations starting from an initial state (the stated problem) and leading to a final state (the problem once solved). Thus, the issue of case adaptation can be addressed by studying the issue of plan adaptation. Finally, it is shown how case retrieval and case adaptation can be related thanks to reformulations and similarity paths.

Failure Analysis for Domain Knowledge Acquisition in a Knowledge-Intensive CBR System

A knowledge-intensive case-based reasoning system has profit of the domain knowledge, together with the case base. Therefore, acquiring new pieces of domain knowledge should improve the accuracy of such a system. This paper presents an approach for knowledge acquisition based on some failures of the system. The cbr system is assumed to produce solutions that are consistent with the domain knowledge but that may be inconsistent with the expert knowledge, and this inconsistency constitutes a failure. Thanks to an interactive analysis of this failure, some knowledge is acquired that contributes to fill the gap from the system knowledge to the expert knowledge. Another type of failures occurs when the solution produced by the system is only partial: some additional pieces of information are required to use it. Once again, an interaction with the expert involves the acquisition of new knowledge. This approach has been implemented in a prototype, called FrakaS , and tested in the application domain of breast cancer treatment decision support.

Representing Knowledge for Case-Based Reasoning: The ROCADE System

This paper presents the object-based knowledge representation system Rocade, that is aimed at the development of case-based reasoning (CBR) systems. CBR is studied by reference to the two levels defined by Newell: at the knowledge level, a general detailed model of the CBR process has been proposed. This model is intended to be implemented at the symbol level materialized by the ROCADE system. This paper presents these two complementary levels and focuses on ROCADE. The concepts and reasoning mechanisms of ROCADE are described, as well as its architecture. Then, its architecture allowing different ways to use it is presented. ROCADE is illustrated with examples of two CBR systems. The implementation of 2 CBR systems are used to illustrate the rocade system the functionalities of the rocade system.