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

Distributed case-based reasoning architectures have the potential to improve the overall performance of case-based reasoning systems.

In this paper we explore the reuse of components of known good schedules in new scheduling problems. This involves accumulating a case-base of good quality schedules, retrieving a case (or cases) similar to a new scheduling problem and building a new schedule from components of the retrieved cases. We start by introducing the components of Case-Based Reasoning (CBR) and we describe a CBR solution to a Travelling Salesman Problem in order to illustrate the use of CBR in optimisation problems. Two CBR solutions to a single machine scheduling problem with sequence dependent setup times are described. These are evaluated by comparing them with two more conventional alternative techniques -- simulated annealing and myopic search. Both CBR techniques are shown to provide good quality solutions quickly. 2 Case-Based Reasoning in Scheduling: Reusing Solution Components 1 Introduction There is a lot of optimism at the moment about the usefulness of case-based reasoning (CBR) in the develop...

Recent explosion in networked information resources has been attracting much attention to the problem of automated methods for gathering information in response to a query from a user. However, most of this literature deals with locating, gathering and selecting the best response to a query from among a multitude of responses from different sources. In this paper, we deal with a different model of response to a query. No single source of information may contain the complete response to a query and hence may necessitate piecing together mutually related partial responses from disparate and possibly heterogeneous sources. We present a system for cooperative retrieval and composition of a case in which sub-cases are distributed across different agents in a multi-agent system. From a Gestalt perspective, a good overall case may not be the one derived from the summation of best subcases. Each agent's partial view may result in local cases that are best matches based on the local view. However, these local cases when assembled may not result in the best overall case in terms of global measures. We propose a negotiation-driven case retrieval algorithm as an approach to dynamically resolving inconsistencies between different case pieces during the retrieval process. The work reported here is supported in part by NSF Center for Intelligent Information Retrieval, ARPA contract N00014-92-J-1698, Office of Naval Research contract N00014-92-J-1450, the National Science Foundation contract CDA 8922572. The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred. 1

. Dj Vu is a case-based software design system whose success is based on two novel techniques: hierarchical case-based reasoning and adaptation-guided retrieval. Hierarchical case-based reasoning (HCBR) solves complex problems in a hierarchical fashion by reusing and combining the solutions of many separate cases and is fully supported by the normal CBR retrieve-and-adapt cycle. Adaptation-guided retrieval (AGR) makes use of adaptation knowledge during retrieval to find the most adaptable case to the target in the case-base and to make predictions about the type of adaptations that will be needed to reuse that case. This technique overcomes the failures that can occur in traditional retrieval methods using semantic-similarity. The paper will describe HCBR and AGR in the context of Dj Vu's plant-control software design domain. Experimental evidence is provided to support our claims of improved problem solving performance and flexibility. 1 Introduction In design and planning ...

Although case-based reasoning (CBR) was introduced as an alternative to rule-based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem-solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of “what if ” analysis needed for creative design. The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria. We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.

This thesis is concerned with the development of an under-lying model of memory to support selective case retrieval for case-based reasoning. The major requirements are that retrieval should be highly flexible yet efficient. The traditional approach of "indexing" is rejected as being too restrictive while more flexible approaches in analogical reasoning are generally too computationally expensive. Several important organisational principles are developed in the memory model. A network representation is advocated with a number of required extensions; such as multi-granular representation, context-based segregation and a statistically-based grading of paths. The organisation of memory offers the potential for the serial performance of a number of retrieval tasks that have previously only been addressed by assuming a massively parallel implementation. The retrieval mechanism developed is a novel activation passing technique that creates a gradation of stored cases during retrieval. Empiri...

CBR seems well suited to fault diagnosis because diagnostic episodes naturally form cases and much of expert competence seems to be based on reuse of old solutions.

Case-Based Reasoning (CBR) is the process of solving a given problem based on the knowledge gained from solving precedents. It is an effective technique in the area of customer services or helpdesks. That is, a CBR system is used to solve most of the commonly occurring customer problems. While the implementation techniques may vary, most CBR systems include the following five steps: case representation and storage, precedent matching and retrieval, adaptation of the retrieved solution, validation of the solution, and finally, casebase update to include the information gained from the new problem. This paper details the various implementation techniques for these five steps, while focusing on a particular helpdesk system, namely SmartUSA, developed for the Union Camp Corporation. This system solves a customer’s problem by filtering the problem description through an alias table to generate a brief description and then matching the brief description with the cases in the database. It has proved to be an effective and user-friendly system that has successfully handled different descriptions of the same problem and allowed for the casebase to be built in free-format (plain) text. This system has significantly reduced the workload and the response time in the customer services department of the Union Camp Corporation. KEYWORDS: Case-based reasoning, machine learning, expert system, artificial intelligence, helpdesk service automation.

Most work in artificial intelligence and law has concentrated on modelling the type of reasoning done by trial lawyers. In fact, most lawyers' work involves planning -- for example, wills and trusts, real estate deals, and business mergers and acquisitions. Certain planning issues, such as the use of underspecified, or "open-textured" rules, are illustrated especially clearly in this domain. In this thesis, I set forth the characteristic features of planning in law, place it in the context of past artificial intelligence work in both law and planning, and describe CHIRON, a system that I have developed implementing my theory of open-textured planning in the domain of personal income tax law.