Introduction Interactive Case-based Reasoning

Applied Intelligence 14, 7–8, 2001 c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. °

Introduction: Interactive Case-Based Reasoning DAVID W. AHA Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Code 5510, 4555 Overlook Ave, SW, Washington, DC 20375-5337, USA [email protected]

´ ˜ HECTOR MUNOZ-AVILA Department of Computer Science, University of Maryland, College Park MD 20742-3255, USA [email protected]

Case-based reasoning (CBR) is an AI problem-solving paradigm that stresses the reuse of stored cases, comprising of hproblem,solutioni pairs, to solve new, similar problems. Important technical issues related to this subject include representation, indexing, retrieval, revision, and retention. Currently, the most important international CBR meetings include the international CBR conferences and the European CBR Workshops, and workshops have also been held in several countries (e.g., the United States, Germany, United Kingdom, Italy). This special issue addresses interactive CBR research, which we define as an extension of the CBR paradigm in which a user is actively involved with the inferencing process. Interest in interactive CBR has recently increased, in large part due to commercial motivation; the most commercially successful application of CBR tools has targeted the customer support market niche [1]. This has been pursued vigorously by Inference Corporation and, more recently, several other companies that market CBR shells. Help desk systems are typically interactive; they require interaction between customers and, for example, call center personnel. Although the CBR techniques in these systems have been historically simple from a researcher’s perspective, this does not imply a lack of interesting applied research issues. In particular, interactive CBR tools must address several topics not addressed by non-interactive tools (e.g., dialogue management, user modeling) and, due to their applied nature, must address integration issues with additional systems.

For example, perhaps the most popular type of interactive CBR systems are what we refer to as conversational CBR (CCBR) systems, which can be characterized as interactive systems that, via a mixed-initiative dialogue, guide users through a question-answering sequence in a case retrieval context. Although Aha and Breslow [2] helped to popularize the phrase “CCBR”, their inspiration was from Inference Corporation, and other groups had been previously researching this topic (e.g., [3]) or began studying it at approximately the same time (e.g., [4]). Four papers in this special issue relate to CCBR. We include ours first because it summarizes research on simple CCBR tools, focusing on contributions for simplifying the case authoring process, enhancing human-machine conversations, and extending CCBR to address decision support tasks. In the next article, Shimazu, Shibata, and Nihei describe recent advances in ExpertGuide, a more advanced CCBR tool, focusing on demonstrating how CCBR tools can be used to develop WWW mentoring systems in a knowledge management context. They describe multilink retrieval capabilities to allow libraries to be searched from multiple viewpoints, entropy algorithms for ranking questions, and indexing cases using scripts. Next, Yang and Wu address problems with very large case bases, and describe advances to CaseAdvisor. They introduce a real time algorithm for creating a decision forest to cluster cases, and then use an information gain approach to select questions. These articles all include empirical evaluations that demonstrate the utilities of the algorithms described.

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Aha and Mu˜noz-Avila

McSherry’s article differs from the others; it analyzes needs for CCBR systems in the context of sequential diagnosis tasks and describes how relevant advances in rule-based expert systems can be used to improve CCBR behavior. His focus is CBR-Strategist, which embodies these advances. The remaining articles focus on interactive CBR in a broader context. For example, Leake and Wilson’s DRAMA system is exciting for its introduction of concept maps to the CBR literature, and in demonstrating how they can be used to support interactive retrieval and adaptation (i.e., in the context of aerospace design tasks). McKenna and Smyth then extend their well-known research on case competence modeling by demonstrating how a visualization tool, in their CASCADE program, can provide valuable feedback to users during the case authoring process. We believe this pioneering work will inspire several others to investigate how other visualization techniques can synergize with interactive CBR tools. The final article describes the latest developments of SaxEx, an impressive CBR tool for increasing the expressiveness of musical phrases. In their article, Arcos and L´opez de M´antaras describe how users can interactively parameterize the system, and report on the utility of this functionality. This first special issue devoted to interactive CBR is highly appropriate for Applied Intelligence, given its application orientation. Researchers studying this subject have recognized that this focus on user interaction provides a rich source for researchers interested in developing tools with high potential for practical application. We are very happy to have attracted six contributions from some of the most experienced groups in the CBR field; their articles are representative of the state-of-the-art in case-based reasoning, and address many exciting topics. We hope that others will also enjoy reading papers from this issue, and invite them to contribute to interactive CBR.

Many thanks to the Editor-in-Chief of Applied Intelligence, Professor Moonis Ali, for providing us with this opportunity. Thanks also to NCARAI/NRL, the University of Maryland, and the Office of Naval Research for supporting our efforts. But most of all, thanks to the authors for their terrific contributions! References 1. I. Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann: San Francisco, 1997. 2. D.W. Aha and L.A. Breslow, “Refining conversational case libraries,” in Proceedings of the Second International Conference on Case-Based Reasoning, Springer: Providence, RI, pp. 267– 278, 1997. 3. H. Shimazu, A. Shibata, and K. Nihei, “Case-based retrieval interface adapted to customer-initiated dialogues in help desk operations,” in Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI Press: Seattle, WA, pp. 513–518, 1994. 4. K. Racine and Q. Yang, “Maintaining unstructured case bases,” in Proceedings of the Second International Conference on CaseBased Reasoning, Springer: Providence, RI, pp. 553–564, 1997. David W. Aha (UCI, 1990) leads projects on planning, case-based reasoning (CBR), and knowledge management. He has (co-) organized ten meetings related to these areas, including serving as Program Co-Chair for ICCBR’01. He is an editor for Machine Learning (ML), on the editorial board for Applied Intelligence, and edited a special quintuple journal issue on Lazy Learning (AI Review, 1997). He is the Head of the Intelligent Decision Aids Group at NRL/NCARAI, where he leads several projects related to mixedinitiative planning and intelligent lessons learned systems. H´ector Munoz-Avila ˜ (U. Kaiserslautern, 1998) is currently working on projects related to mixed-initiative planning in dynamic, real-world domains that require multi-model reasoning approaches. While working with groups at the Naval Research Laboratory and the University of Maryland, he has contributed significantly to the design and development of the HICAP plan authoring tool suite and the SHOP generative planner. Hector’s areas of expertise include case-based reasoning, planning and machine learning, and he often contributes publications to and serves as a reviewer for several conferences and journals related to these areas.