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20. september 2006 TDT55 - Case-based reasoning 1
Retrieval, reuse, revision, and retention in
case-based reasoning
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20. september 2006 TDT55 - Case-based reasoning 2
Introduction
CBR• Influenced by cognitive science• Usage of remindings
(”This reminds me of something I’ve seen before”)
• An important issue is how closely CBR systems should mirror how humans think
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Introduction
The steps of the CBR cycle• Retrieval in CBR
→ Fetches previous cases that are assumed to be able to contribute to solve the target problem
• Reuse→ Suggests a solution for the target-case from the solutions of the retrieved cases, possibly with an adaption process to fit the target-case better
• Revision→ Evaluates the chosen solution with respect to degree of success
• Retention→ The product of the most recent problem-solving episode is incorporated into the system’s knowledge
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Retrieval in CBR
Similarity assessment• Surface features: the features given as a part of
the case description• Similarity-based retrieval is retrieval based on
similarity of the surface features• Ineffective to scan all cases in the base
→ Foot-print based retrieval→ Validation
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Retrieval in CBR
Retrieval performance• The solution quality is as important as the
retrieval speed• Problems that may influence the quality:
→ inadequate similarity measures→ noise→ missing values in cases→ unknown values in the description of the target problem→ the heterogenity problem – different attributes are used to describe different cases
• Work on how to solve this problem:→ making the similarity measure be the subject of an adaptive learning process→ guiding by domain knowledge
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Retrieval in CBR
Alternatives to similarity-retrieval:• Adaption-guided retrieval
→ Retrieval of the cases which are easiest to adapt
• Diversity-conscious retrieval→ Combines similarity and diversity measures to distinguish between cases of great similarity.
• Compromise-driven retrieval → A case is more acceptable than another if it is closer to the user’s query and it involves a subset of the compromises that the other case involves.
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Retrieval in CBR
Alternatives to similarity-retrieval:• Order-based retrieval
→ Combine preferred values with preference information such as max and min values, and values that the user would prefer not to consider.
• Explanation-oriented retrieval → The goal is to explain how the system reached its conclusions. The easiest way of doing this is to use the explanation of the most similar case.
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Reuse and revision in CBR
• Reuse can be as simple as returning the most similar case, but
• significant differences in target problem vs. retrieved case → need for adaption
• Adaption methods: Substitution adaption
→ exchanges parts of the retrieved solution
Transformation adaption→ changes the structure of the retrieved solution
Generative adaption → derives the new solution by repeating the method used to derive the solution of the
retrieved case
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Retention in CBR
• The simplest form of retention is to just save the problem case and its solution as a new case
• The utility problem:As the case-base grows, every new case will not lead to a lot of new information (overlaps other cases), but will increase the searching time just as much
• Solution in general: → Delete harmful cases from the case base
• Solution in CBR: → Use a competence-model to decide each case’s contribution to the total problem solving competence
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Retention in CBR
Case-base maintenance• Insert two new steps into the CBR cycle:
Review – checks the quality of the system knowledgeRestore – chooses and executes maintenance operations
• Categorization of maintenance policies:→ how they gather data relevant to maintenance decisions→ how they determine when to trigger maintenance operations→ the types of maintenance operations available→ how the maintenance operations are executed
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Conclusions
• There is a significant amount of ongoing research on this subject
• A lot of the research is motivated by awareness of the limitations of the traditional approach