Experience with the growing number of large-scale and long-term case-based reasoning (CBR) applications has led to increasing recognition of the importance of maintaining existing CBR systems. Recent research has focused on case-base maintenance (CBM), addressing such issues as maintaining consistency, preserving competence, and controlling case-base growth. A set of dimensions for case-base maintenance, proposed by Leake and Wilson, provides a framework for understanding and expanding CBM research. However, it also has been recognized that other knowledge containers can be equally important maintenance targets. Multiple researchers have addressed pieces of this more general maintenance problem, considering such issues as how to refine similarity criteria and adaptation knowledge. As with case-base maintenance, a framework of dimensions for characterizing more general maintenance activity, within and across knowledge containers, is desirable to unify and understand the state of the art, as well as to suggest new avenues of exploration by identifying points along the dimensions that have not yet been studied. This article presents such a framework by (1) refining and updating the earlier framework of dimensions for case-base maintenance, (2) applying the refined dimensions to the entire range of knowledge containers, and (3) extending the theory to include coordinated cross-container maintenance. The result is a framework for understanding the general problem of case-based reasoner maintenance (CBRM). Taking the new framework as a starting point, the article explores key issues for future CBRM research.

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Maintaining Case‐Based Reasoners: Dimensions and Directions

Researchers from various fields (among which are Artificial Intelligence, Computer Supported Co-operative Work, Information Retrieval, Software Engineering, and Knowledge Engineering) try to address issues in Organisational Memories (OM). Many of these approaches also identify the meaning of context for information captured inside an OM. Up to now context has not been modelled explicitly as part of an OM. This paper gives an overview of different approaches in OM research and proposes research for recognising, modelling, and retrieving contextual information as part of the OM. The main question behind the presented approach is whether knowledge about the creation or usage context of any piece of information within the organisational memory and knowledge about the current context of any organisational member may be used to effectively enhance the individual's access to organisational information.

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Context Framework - an Open Approach to Enhance Organisational Memory Systems with Context Modelling Techniques.

Endowing an intelligent agent with an episodic memory affords it a multitude of cognitive capabilities. However, providing efficient storage and retrieval in a task-independent episodic memory presents considerable theoretical and practical challenges. We characterize the computational issues bounding an episodic memory. We explore whether even with intractable asymptotic growth, it is possible to develop efficient algorithms and data structures for episodic memory systems that are practical for real-world tasks. We present and evaluate formal and empirical results using Soar-EpMem: a task-independent integration of episodic memory with Soar 9, providing a baseline for graph-based, task-independent episodic memory systems.

/pdf/efficiently-implementing-episodic-memory-x6322qh6vh.pdf

Efficiently Implementing Episodic Memory

Effective retrieval and new indexing method for case based reasoning: Application in chemical process design

This paper proposes a fuzzy-rough method of maintaining Case-Based Reasoning (CBR) systems. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which take the form of fuzzy rules generated by the rough set technique. In paper [1], we have proposed a methodology for case base maintenance which used a fuzzy decision tree induction to discover the adaptation rules; in this paper, we focus on using a heuristic algorithm, i.e., a fuzzy-rough algorithm [2] in the process of simplifying fuzzy rules. This heuristic, regarded as a new fuzzy learning algorithm, has many significant advantages, such as rapid speed of training and matching, generating a family of fuzzy rules which is approximately simplest. By applying such a fuzzy-rough learning algorithm to the adaptation mining phase, the complexity of case base maintenance is reduced, and the adaptation knowledge is more compact and effective. The effectiveness of the method is demonstrated experimentally using two sets of testing data, and we also compare the maintenance results of using fuzzy ID3, in [1], and the fuzzy-rough approach, as in this paper.

/pdf/a-fuzzy-rough-approach-for-case-base-maintenance-2si4itgyfe.pdf

A Fuzzy-Rough Approach for Case Base Maintenance

Efficiently indexing and retrieving cases from a very large case library are major concerns when building a Case-Based Reasoning (CBR) system. Most CBR research has focused on representation of cases, how to identify features that should be used for retrieval; and similarity measurement between values of attributes. In this paper, we propose a method for dynamically creating indices, and, also different similarity-measurement methods for different types of attributes. We also discuss the use of a relational database for representing cases, taxonomy knowledge, and spatial information. Our real world problem domain consists of 2 million incomplete insurance cases, with 30 different attributes. Even though all of these are valid cases, only 10 percent of these policies have lodged claims. These situations create a very complex case base for reasoning and problem solving. In response to this complexity, the approach adopted in building our CBR system involves a considerable amount of statistical pre-analysis of the contents of the case base to generate domain knowledge that could be used by the “Dynamic Index Creation Mechanism”. The main contribution of this paper is in describing the techniques used in our CBR system to dynamically create indices for the purpose of effective case retrieval.

Dynamically Creating Indices for Two Million Cases: A Real World Problem

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Dynamically Creating Indices for Two Million Cases: A Real World Problem