20. september 2006 TDT55 - Case-based reasoning 1

Retrieval, reuse, revision, and retention in

case-based reasoning

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 knowledge

Restore – 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