. Conversational case-based reasoning (CBR) shells (e.g., Inference 's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rather than more problematic rules, and they can be incrementally extended. However, rather than eliminate the knowledge engineering bottleneck, they refocus it on case engineering, the task of carefully authoring cases according to library design guidelines to ensure good performance. Designing complex libraries according to these guidelines is difficult; software is needed to assist users with case authoring. We describe an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance. 1 Introduction Now that CBR shells have attained commercial viability, some researchers have...

Conversational case-based reasoning (CCBR) was the first widespread commercially successful form of case-based reasoning. Historically, commercial CCBR tools conducted constrained human-user dialogues and targeted customer support tasks. Due to their simple implementation of CBR technology, these tools were almost ignored by the research community (until recently), even though their use introduced many interesting applied research issues. We detail our progress on addressing three of these issues: simplifying case authoring, dialogue inferencing, and interactive planning. We describe evaluations of our approaches on these issues in the context of NaCoDAE and HICAP, our CCBR tools. In summary, we highlight important CCBR problems, evaluate approaches for solving them, and suggest alternatives to be considered for future research.

A surprisingly large number of research disciplines have contributed towards the development of knowledge on lazy problem solving, which is characterized by its storage of ground cases and its demand driven response to queries. Case-based reasoning (CBR) is an alternative, increasingly popular approach for designing expert systems that implements this approach. This paper lists pointers to some contributions in some related disciplines that offer insights for CBR research. We then outline a small number of Navy applications based on this approach that demonstrate its breadth of applicability. Finally, we list a few successful and failed attempts to apply CBR, and list some predictions on the future roles of CBR in applications.

. Dialogue inferencing is the knowledge-intensive process of inferring aspects of a user's problem from its partial description. Conversational case-based reasoning (CCBR) systems, which interactively and incrementally elicit a user's problem description, suffer from poor retrieval efficiency (i.e., they prompt the user with questions that the user has already implicitly answered) unless they perform dialogue inferencing. The standard method for dialogue inferencing in CCBR systems requires library designers to supply explicit inferencing rules. This approach is problematic (e.g., maintenance is difficult). We introduce an alternative approach in which the CCBR system guides the library designer in building a domain model. This model and the partial problem description are then given to a query retrieval system (PARKA-DB) to infer any implied answers during a conversation. In an initial empirical evaluation in the NaCoDAE CCBR tool, our approach improved retrieval efficiency without sa...

. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e., precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries. 1 Introduction In the context of the ECML-97 Workshop entitled Case-Based Learning: Beyond Classification of Feature Vectors, this paper's contribution focuses on using machine learning methods to assist in the design of case libraries. These libraries are designed for solution retrieval rather than classification tasks, and each case might contain a unique solution. Cas...