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.

ion in Case-Based Reasoning Ralph Bergmann and Wolfgang Wilke University of Kaiserslautern, Centre for Learning Systems and Applications (LSA) Dept. of Computer Science, P.O.-Box 3049, D-67653 Kaiserslautern, Germany E-Mail: fbergmann,wilkeg@informatik.uni-kl.de Abstract. This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We argue that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation....

Planning for manufacturing workpieces is a complex task that requires the interaction of a domain-specific reasoner and a generic planning mechanism. In this paper we present an architecture for organizing the case base that is based on the information provided by a generic problem solver. A retrieval procedure is then presented that uses the information provided by the domain-specific reasoner in order to improve the accuracy of the cases retrieved. However, it is not realistic to suppose that the case retrieved will entirely fit into the new problem. We present a replay procedure to obtain a partial solution that replays not only the valid decisions taken for solving the case, but also justifications of rejected decisions made during the problem solving process. As a result, those completion alternatives of the partial solution are discarded that are already known to be invalid from the case. Introduction Planning in the domain of process planning has been the subject of wide studi...

This paper describes HICAP, a general purpose and interactive case-based planning architecture. HICAP is a decision support tool for planning a hierarchical course of action. It integrates a hierarchical task editor, HTE, with a conversational case-based planner, NaCoDAE/HTN. HTE maintains a task hierarchy representing guidelines that constrain the final plan. HTE also encodes the hierarchical organization responsible for these tasks. This supports bookkeeping, which is crucial for real-world large-scale planning tasks. HTE can be used to activate NaCoDAE/HTN to interactively refine user-selected guideline tasks into a concrete plan. Our application of HICAP to the task of noncombatant evacuation operations inspired its architecture. In this application, our empirical evaluation with ModSAF simulations confirms that the plans output by HICAP outperform those generated using alternative approaches on three dimensions.

This paper presents an overview of different adaptation methods which are common in today's systems. We introduce first the process model of CBR and the used knowledge according to the different knowledge containers. Next, we present current models of adaptation and illustrate them in an example domain and close with some remarks. 2 Knowledge Modelling for Case-Based Systems Before we have a closer look at different adaptation techniques we have to understand the CBR process during problem solving and the used knowledge sources. This is illustrated in Figure 1. DRAFT

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner dersnlp+ebl. dersnlp +ebl extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgemen...

Case-based planning is the reuse of past successful plans in order to solve new planning problems. This paper presents a survey of case-based planning, in terms of its historical roots, underlying foundations, methods and techniques currently used, limitations, and future trends. Several authors have given overviews on case-based reasoning and specific topics such as case retrieval, case adaptation, and learning. This overview differs in focus. Its aim is to emphasize the case-based approach to planning, its methodological issues, and its relation to classical planning and the other kinds of case-based reasoning. It also provides some reference models. Keywords: Case-based planning, Case-based reasoning, Planning, Plan Retention, Plan Retrieval, Plan Reuse, Plan Revision. Statement of Exclusive Submission: This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Artificial Intelligence Review. 1 Contents 1

We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTN-MAKER takes as input the initial states from a set of classical planning problems in a planning domain and solutions to those problems, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic information in order to determine which portions of the input plans accomplish a particular task and constructs HTN methods based on those analyses. Our theoretical results show that HTN-MAKER is sound and complete. We also present a formalism for a class of planning problems that are more expressive than classical planning. These planning problems can be represented as HTN planning problems. We show that the methods learned by HTN-MAKER enable an HTN planner to solve those problems. Our experiments confirm the theoretical results and demonstrate convergence in three well-known planning domains toward a set of HTN methods that can be used to solve nearly any problem expressible as a classical planning problem in that domain, relative to a set of goals.

In this thesis, a specific memory structure is presented that has been developed for the retrieval task in Case-Based Reasoning systems, namely Case Retrieval Nets (CRNs). This model borrows from associative memories in that it suggests to interpret case retrieval as a process of re-constructing a stored case rather than searching for it in the traditional sense. Tow major advantages of this model are efficiency and flexibility: Efficiency, on the one hand, is concerned with the ability to handle large case bases and still deliver retrieval results reasonably fast. In this thesis, a formal investigation of efficiency is included but the main focus is set on a more pragmatic view in the sense that retrieval should, in the ideal case, be fast enough such that for the users of a related system no delay will be noticeable...

Abstract. In the CBR literature from the past 25 years there is a considerable amount of research work that makes use of cases that are subspaces of some representation space rather than points in it. For cases of that kind, different terms case. Our analysis of selected publications yields that on the one hand the same term is used for different concepts and on the other hand different terms are used for more or less the same concepts. So our goal is to improve the conceptual clarity by proposing an integrated classification schema for cases. We then use this schema to describe semantically founded ways for similarity definition and computation, depending on the class membership of query and case. 1