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A case-based reasoning system can produce both a solution and an estimate of the confidence in that solution. The confidence value can be used to determine whether the solution does or does not have the needed accuracy. A statistical method can be used to compute a confidence value from information generated during the case-based reasoning process. This confidence value allows users to know when the...
In this article we show how the accuracy of a rule based first order theory may be increased by combining it with a case-based approach in a classification task. Case-based learning is used when the rule language bias is exhausted. This is achieved in an iterative approach. In each iteration theories consisting of first order rules are induced and covered examples are removed. The process stops when...
In this paper we describe a domain independent architecture to help in the design of knowledge intensive CBR systems. It is based on the knowledge incorporation from a library of application-independent ontologies and the use of an ontology with the common CBR terminology that guides the case representation and allows the description of flexible, generic and homogeneous CBR processes based on classification.
Online decision guides typically ask too many questions of the user, as they make no attempt to focus the questions. We describe some approaches to minimising the questions asked of a user in an online query situation. Questions are asked in an order that reflects their ability to narrow down the set of cases. Thus time to reach an answer is decreased. This has the dual benefit of taking some of the...
The “similar problem-similar solution” hypothesis underlying case-based reasoning is modelled in the framework of possibility theory and fuzzy sets. Thus, case-based prediction can be realized in the form of fuzzy set-based approximate reasoning. The inference process makes use of fuzzy rules. It is controlled by means of modifier functions acting on such rules and related similarity measures. Our...
In this paper, we look at case retrieval systems for product selection. Such systems are interactive. This places demands on the technology: customers must be able to specify their requirements in ways that are meaningful to them; and, the cases that are retrieved must be comprehensible in terms of the customer requirements. To meet these demands, we introduce to case retrieval the notions of similarity...
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...
In this paper, we describe the Adaptive Place Advisor, a user adaptive, conversational recommendation system designed to help users decide on a destination, specifically a restaurant. We view the selection of destinations as an interactive, conversational process, with the advisory system inquiring about desired item characteristics and the human responding. The user model, which contains preferences...
Case-Based Reasoning is a good framework for Software Reuse because it provides a flexible and powerful searching mechanism for software components. In a CBR system for software reuse it is important to learn the user preferences adapting the system software choices to the user. In a complex domain as software design, the similarity metric will also be complex, thus creating the necessity for a learning...
In order to predict the solution to a new problem we proceed from the “similar problem-similar solution” assumption underlying case-based reasoning. The concept of a similarity hypothesis is introduced as a formal model of this meta-heuristic. It allows for realizing a constraint-based inference scheme which derives a prediction in the form of a set of possible candidates. We propose an algorithm...
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,...
We introduce a distance measure based on the idea that two vectors are considered similar if they lead to similar predictive probability distributions. The suggested approach avoids the scaling problem inherent to many alternative techniques as the method automatically transforms the original attribute space to a probability space where all the numbers lie between 0 and 1. The method is also flexible...
An important focus of recent CBR research is on how to develop strategies for achieving compact, competent case-bases, as a way to improve the performance of CBR systems. However, compactness and competence are not always good predictors of performance, especially when problem distributions are non-uniform. Consequently, this paper argues for developing methods that tie case-base maintenance more...
This paper presents two applications for the breast cancer treatment decision helping. The first one is called Casimir/RBR and can be likened to a rule-based reasoning system. In some situations, the application of the rules of this system does not provide a satisfying treatment. Then, the application Casimir/CBR-which is not fully implementedcan be used. Casimir/CBR uses principles of case-based...
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...
CaseMaker is an interactive environment for intelligent case-authoring support in CREST, a case-based reasoner for estimation tasks, in which the selection of cases for addition to a case library is guided by empirical evaluation of the coverage contributions of candidate cases. We present a new version of the environment called CaseMaker-2 which is designed to support case authoring more effectively...
Some problem-solving tasks are amenable to integrated case retrieval and generative planning techniques. This is certainly true for some decision support tasks, in which a user controls the problem-solving process but cannot provide a complete domain theory. Unfortunately, existing integrations are either non-interactive or require a complete domain theory and/or complete world state to produce acceptable...
In this paper, we propose a thorough investigation of a nearest neighbor rule which we call the “Symmetric Nearest Neighbor (sNN) rule”. Basically, it symmetrises the classical nearest neighbor relationship from which are computed the points voting for some instance. Experiments on 29 datasets, most of which are readily available, show that the method significantly outperforms the traditional Nearest...
The definition of suitable case base maintenance policies is widely recognized as a major success key of CBR systems; underestimating this issue may lead to systems that that do not perform adequately under performance dimensions, namely computation time, competence and quality of solutions. The goal of the present paper is to analyse an automatic case base management strategy in the context of multi-modal...
Case base maintenance is one of the most important issues for current research in Case-Based Reasoning (CBR). In this paper, we outline two novel steps as part of the maintenance phase of the CBR process. The review step covers assessment and monitoring of the knowledge containers whereas the restore step actually modifies the contents of the containers according to recommendations resulting from...
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