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6.2. CASE INDEXINGIndexing allows the cases to be retrieved selectively, quickly, and accurately. Identifying the relevant features to be indexed is not trivial, especially for indexes that are not just surface or shallow features. Retrieving the correct or relevant case is crucial to the success of CBR to solve new problems. Once cases are represented and indexed, they need to be stored and organized in an efficient structure for fast and accurate retrieval. Memory may be organized as a simple list, or a discrimination or dependency graph in order to reduce the search effort, and the most efficient retrieval algorithm determined. Another problem is the uncontrolled growth of the case base. How do we determine which cases to store, and which are to be weeded out? Essentially, what makes a "good" case? Currently, maintaining the case library is carried out manually, with a case-base administrator browsing through each case and deciding whether to keep it or remove it. While there are simple procedures (such as time-stamping) to assist the administrator, it is still a time-consuming process, especially with thousands of cases in the library. 6.3. CASE ADAPTATION AND LEARNINGA CBR system learns through adaptation, i.e., modifying a retrieved case (that partially matches the current problem) to solve the new problem. This means that over time, new cases are acquired, and the system's problem-solving performance should improve as it "learns." Learning may be from positive examples given by the domain expert, or from its own success (or even from its failure) in achieving the problem-solving goal. However, adaptation formulas are not always easy to define, even by a domain expert. How does the case-based reasoner know what to adapt, and when? Can adaptation rules that are sufficiently generic (for all situations) be built such that the case-based reasoner is able to detect the differences between the new and retrieved case, decide what needs to be adapted, and determine which adaptation strategy to choose (from several appropriate strategies) and project the likelihood of succeeding? In summary, some of the open issues and research challenges are in the areas of case representation, similarity matching, selecting indices and generating new indices dynamically, case library organization (as new cases are encountered and entered into the library), as well as mixing CBR with other paradigms to build hybrid systems (see Section 7). 7. EMERGING HYBRID SYSTEMSCBR hybrid systems capitalize on the inherent strengths of the other reasoning paradigms to counter the weaknesses in CBR, and complement its strengths. The most common combination attempted at this point in time is combining rule-based reasoning (RBR) with CBR, and model-based reasoning with CBR. 7.1. CBR AND RULE-BASED REASONING (RBR)Rules of the form IF-THEN-ELSE cover a single aspect of knowledge, whereas a case covers a particular problem-solving situation. It is sometimes difficult for a domain expert to come up with rules to problem-solving, whereas cases are actually examples of a particular situation. Hence, in general, acquiring cases are easier than rules. RBR has some inherent weaknesses:
Wrong information in rules must be individually edited, and every time a new rule is added, there is the risk of redundancy or contradiction. Deleting a rule may interrupt the reasoning process, whereas removing an individual case does not have the same extent of consequence. Hence, RBR systems are more difficult to maintain, and more difficult to update to keep up with rapidly changing domains. Thus, most hybrid systems have a combination of rules and cases, and may be according to whether rules and cases are independent, or whether one was derived from the other (i.e., derive cases from rules, or rules from a set of sample cases). Rules may be used to capture broad trends in the domain, while cases are used to support exceptions to the rules or violations to rules. Cases may be kept as positive examples to support, explain, or justify rules; or as counter-examples to rules; or when rules fail because they are ill-defined. However, there are problems integrating cases and rules, and these have to do with control -- which mode of reasoning to use, and when. As yet, there is still a lack of cognitive models of how human experts integrate rules and cases. 7.2. CBR AND INFORMATION RETRIEVALAn AAAI symposium was organized in the spring of 1993 at Stanford University to see how CBR can benefit from the experiences of the Information Retrieval (IR) community. While there were interesting discussions on what may be useful for CBR to adapt from the IR research, no conclusions were reached. IR researchers have development indexing mechanisms that can successfully manage the storage and retrieval of a large number of documents, where the documents can be of any size and are normally considered to be unstructured data. They have considerable experience in defining similarity measures across several features and have also been good at identifying relevance feedback methods for modifying search results. These are areas that CBR can adopt from IR and adapt to fit its own requirements. What CBR needs in addition is the need to store structured data, methods for modifying a retrieved case, and the need to know when a new problem case should be documented as a new case and not as a minor adaptation of an old case. These are areas where the research results and the application development experience from the IR community cannot help the CBR application.
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