Susan Craw

Retrieval, reuse, revision and retention in case-based reasoning

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

Implementation of a spatial decision support system for rural land use planning: integrating geographic information system and environmental models with search and optimisation algorithms

Abstract The implementation of a spatial decision support system (DSS) developed as a tool for rural land use planning at the management unit level is described. The DSS fulfils the need for a tool that allows rural land managers to explore their land use options and the potential impacts of land use change. The DSS is based on five components: a geographic information system (GIS); land use modules; impact assessment modules; a graphical user interface; and land use planning tools. These components are implemented across two software platforms Gensym’s G2 knowledge based system (KBS) development environment and Smallworld GIS. Following a review of the DSS components, the paper focuses on two aspects. First, the use of the object-orientation paradigm to facilitate the integration of geospatial information. Second is the proposed use of genetic algorithms, a class of search and optimisation algorithm, to find optimum land use plans using the integrated functionality of both KBS and GIS.

Learning adaptation knowledge to improve case-based reasoning

Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.

Genetic Algorithms to Optimise CBR Retrieval

Knowledge in a case-based reasoning (CBR) system is often more extensive than simply the cases, therefore knowledge engineering may still be very demanding. This paper offers a first step towards an automated knowledge acquisition and refinement tool for non-case CBR knowledge. A data-driven approach is presented where a Genetic Algorithm learns effective feature selection for inducing case-base index, and feature weights for similarity measure for case retrieval. The optimisation can be viewed as knowledge acquisition or maintenance depending on whether knowledge is being created or refined. Optimising CBR retrieval is achieved using cases from the case-base and only minimal expert input, and so can be easily applied to an evolving case-base or a changing environment. Experiments with a real tablet formulation problem show the gains of simultaneously optimising the index and similarity measure. Provided that the available data represents the problem domain well, the optimisation has good generalisation properties and the domain knowledge extracted is comparable to expert knowledge.

Design, innovation and case-based reasoning

The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning and survey research on case-based design ranging from early explorations to more recent work on innovative design. We also summarize the theoretical contributions this research has made to case-based reasoning itself.

Case-Based Design for Tablet Formulation

Case-Based Design (CBD) applies a knowledge-based process to the knowledge commonly associated with Case-Based Reasoning (CBR) systems — the library of exemplars. This paper investigates the problems in using commercial CBR tools, primarily aimed at classification applications, for a more knowledge intensive CBD task, and proposes techniques that overcome some of these difficulties. This work results from the development of a pharmaceutical CBD system Cbr-Tfs that proposes tablet formulations in order to manufacture viable tablets. Results show that Cbr-Tfs proposes useful ingredients for the tablet, and that the quantities it suggests are well within the limits of the tablet manufacturing process. CBDs increased need for specialised adaptation knowledge is also highlighted and this raises the issue of its acquisition.

Integrating case-based reasoning with an electronic patient record system

UNLABELLED Electronic patient records (EPRs) contain a wealth of patient-related data and capture clinical problem-solving experiences and decisions. Excelicare is such a system which is also a platform for the national generic clinical system in the UK. OBJECTIVE This paper presents, ExcelicareCBR, a case-based reasoning (CBR) system which has been developed to complement Excelicare. Objective of this work is to integrate CBR to support clinical decision making by harnessing electronic patient records for clinical experience reuse. METHODS CBR is a proven problem solving methodology in which past solutions are reused to solve new problems. A key challenge that we address in this paper is how to extract and represent a case from an EPR. Using an example from the lung cancer domain we demonstrate our generic case representation approach where Excelicare fields are mapped to case features. Once the case base is populated with cases containing data from the EPRs database a standard weighted k-nearest neighbour algorithm combined with a genetic algorithm based feature weighting mechanism is used for case retrieval and reuse. CONCLUSIONS We conclude that incorporating case authoring functionality and a generic retrieval mechanism were key to successful integration of ExcelicareCBR. This paper also demonstrates how the application of CBR can enable sharing of lessons learned through the retrieval and reuse of EPRs captured as cases in a case base.

Learning to Adapt for Case-Based Design

Design is a complex open-ended task and it is unreasonable to expect a case-base to contain representatives of all possible designs. Therefore, adaptation is a desirable capability for case-based design systems, but acquiring adaptation knowledge can involve significant effort. In this paper adaptation knowledge is induced separately for different criteria associated with the retrieved solution, using knowledge sources implicit in the case-base. This provides a committee of learners and their combined advice is better able to satisfy design constraints and compatibility requirements compared to a single learner. The main emphasis of the paper is to evaluate the impact of specific-to-general and general-to-specific learning on adaptation knowledge acquired by committee members. For this purpose we conduct experiments on a real tablet formulation problem which is tackled as a decomposable design task. Evaluation results suggest that adaptation achieves significant gains compared to a retrieve-only CBR system, but shows that both learning biases can be beneficial for different decomposed sub-tasks.

Self-optimising CBR retrieval

One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effort may be demanding. In this paper we present a tool which helps to reduce the knowledge acquisition effort for building a typical CBR retrieval stage consisting of a decision-tree index and similarity measure. We use genetic algorithms to determine the relevance/importance of case features and to find optimal retrieval parameters. The optimisation is done using the data contained in the case-base. Because no (or little) other knowledge is needed this results in a self-optimising CBR retrieval. To illustrate this we present how the tool has been applied to optimise retrieval for a tablet formulation problem.

Maintaining retrieval knowledge in a case-based reasoning system.

The knowledge stored in a case base is central to the problem solving of a case‐based reasoning (CBR) system. Therefore, case‐base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest‐neighbor algorithms used by CBR retrieval. Optimization is applied after case‐base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the companys formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case‐base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.