The contributions at the AAAI-99 Workshop on Exploring Synergies of Knowledge Management and Case-Based Reasoning (www.aic.nrl.navy.mil/~aha/aaai99-kmcbrw) were varied; it was the first AAAI workshop that focused at the intersection of these two disciplines. In this paper I attempt to identify trends of interest to the case-based reasoning community, as exhibited by this workshop's contributions, and predict some future trends. 1. Context The AAAI-99 KM/CBR Workshop took place on 19 July 1999 in Orlando, Florida (USA) as part of the AAAI-99 Conference. Our goal for this workshop was to improve our understanding of knowledge management (KM) objectives and in how case-based reasoning (CBR) techniques could be used to assist in solving them. Given this, we designed the workshop to include several invited KM talks that were tutorial in nature, with the expectation that most of the submissions would be devoted to demonstrations of incorporating CBR techniques in KM applications. The proce...

Agent technology provides many services to users. The tasks in which agents are involved include information filtering, information retrieval, user's tasks automation, browsing assistance and so on. In order to assist users, agents have to learn their preferences. These preferences are represented by user profiles. Many techniques have been developed for user profiling, which vary from statistical keyword analysis to social filtering algorithms and different machine learning techniques. This paper presents a technique that integrates Case-Based Reasoning and Bayesian Networks to build user profiles incrementally. Case-Based Reasoning provides a mechanism to acquire knowledge about user actions that are worth recording to determine his habits and preferences. Bayesian Networks provide a tool to model quantitative and qualitative relationships between items of interest. Information needed to build the BN is taken from cases stored in the case base. This technique supports particularly users'routines and changes of interests over time.

Abstract In this paper we discuss different architectures for reasoning under uncertainty related to our ongoing research into building a medical decision support system. The uncertainty in the medical domain can be divided into a well understood part and a less understood part. This motivates the use of a hybrid decision support system, and in particular, we argue that a Bayesian network should be used for those parts of the domain that are well understood and can be explicitly modeled, whereas a case-based reasoning system should be employed to reason in parts of the domain where no such model is available. Four architectures that combine Bayesian networks and case-based reasoning are proposed, and our working hypothesis is that these hybrid systems each will perform better than either framework will do on its own. 1

Feature Weighting is one of the most difficult tasks when developing Case Based Reasoning applications. This complexity grows when dealing with ill-defined wide domains with a sparse case base. Moreover, most widely-used feature selection and feature weighting methods assume that features are either relevant in the whole instance space or irrelevant through-out. However, it is often the case that specific features are only relevant within the context of other features' values (i.e., feature Y is relevant if feature X=1, but irrelevant if X=0). Therefore, features' weight and relevance are Context-Sensitive. This paper defines a model...

When using knowledge-intensive case-based reasoners, a key issue is to keep the knowledge-base updated. This is usually done by having the user update the knowledge during the retain phase, but machine-learning methods can also be used. In this paper we discuss how an elaboration phase, in which plausible inference rules are derived, can be done in a goal-driven manner to increase the system‟s automatic learning capability. 1

A challenge of future knowledge management and decision support systems is to combine the storage and effective reuse of data, systematically captured as process or system information, with user experience in dealing with problems and non-trivial situations. In CBR, situation-specific user experiences are typically captured in cases. In our approach, cases are linked within a semantic network of more general domain knowledge. In this paper we present a way to automate the construction and dynamical refinement of such a model of case-specific and general knowledge, on the basis of external process data continuously being generated. A data mining method based on a Bayesian Networks approach is used. We are also looking into how the notion of causality, being a central issue in both BNs and model-based AI, can be compared and better understood by relating it to such a combined model.

Abstract—The overall goal of this research is to study reasoning under uncertainty by combining Bayesian Networks and Case-Based Reasoning through constructing an experimental decision support system for classification of cancer pain. We have experimentally analysed a medical dataset in order to reveal properties of the data with respect to properties of the two reasoning methods. We also preprocessed our medical data with help from a clinical expert, which resulted in four data sets with different characteristics. This culminates in a hybrid system architecture, where CBR handles the exceptions or outliers with respect to the distribution of the data and the target class, while BN handles the more common situations. Through a set of experiments under varying conditions we show that a hybrid BN+CBR system is favorable over each single method.