Textual-case based reasoning (TCBR) systems where the problem and solution are in free text form are hard to evaluate. In the absence of class information, domain experts are needed to evaluate solution quality, and provide relevance information. This approach is costly and time consuming. We propose three measures that can be used to compare alternate TCBR system configurations, in the absence...

In this paper, we argue that syntactic analysis is most likely to improve retrieval accuracy in textual case-based reasoning when the task of the system is well-defined and the relationship between queries and cases is specified in terms of this task. We illustrate this claim with an implemented system for syntax-based answer-indexed retrieval, RealDialog.

This paper presents an integrative agent model for adaptive human-aware information presentation. Within the agent model, meant to support humans in demanding tasks, a domain model is integrated which consists of a dynamical model for human functioning, and a model determining the effects of information presentation. The integrative agent model applies model-based reasoning methods to the domai...

This is the first paper on textual case-based reasoning to employ collective classification, a methodology for simultaneously classifying related cases that has consistently attained higher accuracies than standard classification approaches when cases are related. Thus far, case-based classifiers have not been examined for their use in collective classification. We introduce Case-Based Collecti...

The case-based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the diierent learning mechanisms interact and their combined utility. This paper presents a case study examining the r...

ion Abstraction Measurement Interpretation of data Prediction Model Construction:

We motivate and present similarity metrics, the similarity measurement framework we have developed for use within CaseBased Reasoning Systems. In this framework similarities are values from any type on which a complete lattice is defined. This gives us a wide range of intuitive ways of measuring similarity and a large number of ways in which different metrics can be combined. The paper conclude...

A new approach for synthesis of separation sequences by case-based reasoning (CBR) is presented. CBR is a method of finding the most similar existing designs and applying the knowledge of their concept and design for solving new problems. The method has earlier been applied to selecting single separations and simple sequences but has now been extended to cover synthesis of more complicated syst...