Elizabeth McKenna

Modelling the Competence of Case-Bases

The competence of a case-based system (the range of problems it can solve) depends critically on the cases in the case-base. However, the precise relationship between cases and overall competence is a complex one. For example, some cases can be critical to competence, while others may be largely redundant. In this paper we present, and evaluate, a new model of case competence. We argue that this model has an important role to play in areas such as the evaluation and benchmarking of case-based techniques, and we demonstrate a novel application of the model as a guide to case-base designers during the case authoring process.

Building Compact Competent Case-Bases

Case-based reasoning systems solve problems by reusing a corpus of previous problem solving experience stored as a case-base of individual problem solving cases. In this paper we describe a new technique for constructing compact competent case-bases. The technique is novel in its use of an explicit model of case competence. This allows cases to be selected on the basis of their individual competence contributions. An experimental study shows how this technique compares favorably to more traditional strategies across a range of standard data-sets.

Footprint-Based Retrieval

The success of a case-based reasoning system depends critically on the performance of the retrieval algorithm used and, specifically, on its efficiency, competence, and quality characteristics. In this paper we describe a novel retrieval technique that is guided by a model of case competence and that, as a result, benefits from superior efficiency, competence and quality features.

Competence Models and the Maintenance Problem

Case‐based reasoning (CBR) systems solve problems by retrieving and adapting the solutions to similar problems that have been stored previously as a case base of individual problem solving episodes or cases. The maintenance problem refers to the problem of how to optimize the performance of a CBR system during its operational lifetime. It can have a significant impact on all the knowledge sources associated with a system (the case base, the similarity knowledge, the adaptation knowledge, etc.), and over time, any one, or more, of these knowledge sources may need to be adapted to better fit the current problem‐solving environment. For example, many maintenance solutions focus on the maintenance of case knowledge by adding, deleting, or editing cases. This has lead to a renewed interest in the issue of case competence, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that ultimately any generic maintenance solution must explicitly incorporate competence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case competence that can provide a sound foundation for future maintenance solutions. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative and successful maintenance solutions to a variety of different maintenance problems.

Competence-Guided Case-Base Editing Techniques

Case-based classification is a powerful classification method, which (in its simplest form) assigns a target case to the same class as the nearest of n previously classified cases. Many case-based classifiers use the simple nearest-neighbour technique to identify the nearest case, but this means comparing the target case to all of the stored cases at classification time, resulting in high classification costs. For this reason many techniques have been proposed to improve the performance of case-based classifiers by reducing the search they must perform. In this paper we will look at editing techniques that preserve the lazy-learning quality of case-based classification, but improve classification performance.

Competence guided incremental footprint-based retrieval

Abstract Case-based reasoning (CBR) systems solve new problems by retrieving and adapting problem solving experiences stored as cases in a case-base. Success depends largely on the performance of the case retrieval algorithm used. Smyth and McKenna [Lecture Notes in Artificial Intelligence LNAI 1650 (1999) 343–357] have described a novel retrieval technique, called footprint-based retrieval (FBR), which is guided by a model of case competence. FBR as it stands benefits from superior efficiency characteristics and achieves near-optimal competence and quality characteristics. In this paper, we describe a simple but important extension to FBR. Empirically we show that this new algorithm can deliver optimal retrieval performance while at the same time retaining the efficiency benefits of the original FBR method.

An Interactive Visualisation Tool for Case-Based Reasoners

Case-based reasoning (CBR) offers many opportunities for human interaction as part of its reasoning cycle. In particular, one of the main advantages of case-based methods is their use of real case data, the sort of data that humans are intrinsically comfortable with—this is typically in contrast to the rule-based and model-based knowledge of more traditional first-principles reasoning systems. As a result, human participation has been a key factor in a number of case-based systems, particularly when it comes to assisting in the retrieval and adaptation processes. In this article we consider the case authoring process and note that, although the authoring process has always been driven by human involvement, it is probably the least well developed CBR process when it comes to offering real-time assistance to the human author. Many conventional CBR authoring tools provide editing and auditing facilities only. In this article we describe the innovative approach behind the CASCADE authoring system, which allows case authors to interact with, and be guided by, a model of case competence through a variety of novel visualisation tools. We argue that this mode of interaction facilitates the more rapid development of high quality case bases.

An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners

Case-based reasoning systems solve new problems by reusing previous problem solving experience stored as cases in a case-base. In recent years the maintenance problem has become an increasingly important research issue for the case-based reasoning community. In short, the goal is to develop strategies for effectively maintaining the efficiency and competence of case-based reasoning systems as they evolve. Our research has focused on the development of a model of competence for case-based reasoning systems, a model that measures the contributions of individual cases to overall system competence, and which forms the computational basis for a variety of maintenance strategies. However, while this model offers many potential advantages its upkeep adds an additional cost to the CBR cycle. In this paper we evaluate a new method for more efficiently updating the model at run-time.

Competence-Guided Case Discovery

The performance of a case-based reasoner depends critically on the cases in its case-base. Research to date has focused on those cases that are present in case-bases, with little or no direct attention given to the holes that exist in every case-base, and that ultimately limit the competence of real systems. In this paper we argue that modeling these competence holes is necessary to fully understand the potential of a case-base. We present and evaluate a novel technique for identifying, mapping and filling these competence holes by pro-actively discovering new cases that enhance the competence of the evolving case-base.

Incremental Footprint-Based Retrieval

Case-based reasoning systems solve new problems by retrieving and adapting problem solving experiences stored as cases in a case-base. Success depends largely on the performance of the case retrieval algorithm used. Smyth & McKenna [15] have described a novel retrieval technique, called footprint-based retrieval, which is guided by a model of case competence. Footprint-based retrieval as it stands benefits from superior efficiency characteristics and achieves near-optimal competence and quality characteristics. In this paper we describe a simple but important extension to footprint-based retrieval. Empirically we show that this new algorithm can deliver optimal retrieval performance while at the same time retaining the efficiency benefits of the original footprint-based retrieval method.