Case Based Reasoning

Case-Based Reasoning

Davitkov Miroslav, 2011/3116

University of BelgradeFaculty of Electrical Engineering

1. Case-Based Reasoning definition

Case-Based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems.

CBR is reasoning by remembering: It is a starting point for new reasoning

Case-Based Reasoning is a well established research field that involves the investigation of theoretical foundations, system development and practical application building of experience-based problem solving.

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An auto mechanic who fixes an engine by recalling another car thatexhibited similar symptoms

A lawyer who advocates a particular outcome in a trial based onlegal precedents or a judge who creates case law.

An engineer copying working elements of nature (practicingbiomimicry), is treating nature as a database of solutions toproblems.

Case-based reasoning is a prominent kind of analogy making.

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1. Case-Based Reasoning definition

Everyday examples of CBR :

1. Case previously made and stored experience item

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2. CBR problem solver

2. Case-Base core of every case based problem solver

- collection of cases

One of the core assumptions behind CBR is thatsimilar problems have similar solutions.

A case-based problem solver solves new problems primarilyby reuse of solutions from the cases in the case-base.

For this purpose, one or several relevant cases are selected.

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Once similar cases are selected, the solution(s) from the case(s) are adapted to become a solution of the current problem.


When a new (successful) solution to the new problem is found, a new experience is made, which can be stored in the case-base to increase its competence,thus implementing a learning behavior.

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1. Structural (a common structured vocabulary, i.e. an ontology)

2. Textual (cases are represented as free text, i.e. strings)

3. Conversational(a case is represented through a list of questions that varies from one case to another ; knowledge is contained in customer / agent conversations)

3. Types of CBR

There are three main types of CBR that differ significantlyfrom one another concerning case representation andreasoning:

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4. CBR Cycle

Despite the many different appearances of CBRsystems,

the essentials of CBR are captured in a surprisinglysimple and uniform process model.

The CBR cycle consists of 4 sequential steps around theknowledge of the CBR system.

The CBR cycle is proposed by Aamodt and Plaza.

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4. CBR Cycle

New Case

Retrieved Case

New Case

Solved Case

Tested / Repaired

Case

LearnedCase

General Knowledge

Previous Cases

Problem

SuggestedSolution

ConfirmedSolution

RETRIEVE

REUSE

REVISE

RETAIN

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One or several cases from the case base are selected,based on the modeled similarity.

The retrieval task is defined as finding a small number of cases fromthe case-base with the highest similarity to the query.

This is a k-nearest-neighbor retrieval task considering a specificsimilarity function.

When the case base grows, the efficiency of retrieval decreases => methods that improve retrieval efficiency, e.g. specific index structures such as kd-trees, case-retrieval nets, or discrimination networks.

4.1. Retrieve

4. CBR Cycle

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Reusing a retrieved solution can be quite simple if the solution isreturned unchanged as the proposed solution for the new problem.

Adaptation (if required, e.g. for synthetic tasks).

Several techniques for adaptation in CBR

- Transformational adaptation

- Generative adaptation

Most practical CBR applications today try to avoid extensiveadaptation for pragmatic reasons.

4.2. Reuse

4. CBR Cycle

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In this phase, feedback related to the solution constructed so far isobtained.

This feedback can be given in the form of a correctness rating of theresult or in the form of a manually corrected revised case.

The revised case or any other form of feedback enters the CBRsystem for its use in the subsequent retain phase.

4.3. Revise

4. CBR Cycle

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The retain phase is the learning phase of a CBR system (adding arevised case to the case base).

Explicit competence models have been developed that enable theselective retention of cases (because of the continuous increase of thecase-base).

The revised case or any other form of feedback enters the CBRsystem for its use in the subsequent retain phase.

4.4. Retain

4. CBR Cycle

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5. CBR and the Future Internet

The development of the future internet is affected by two majorfactors: semantics and collaboration.

Two of the most influencing developments of the Semantic Web are:- the resource description language RDF (Resource Description

Framework)

- the knowledge representation language OWL (Web OntologyLanguage), which is based on RDF

Already before the development of RDF and OWL, XML has beenused as a case representation within the case-based reasoningcommunity.

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There is a notable similarity between the ontologies developedwithin semantic applications and the representation of cases instructural case-based reasoning.

Due to this similarity RDF and OWL both lend themselves to beused as case representation languages and thus expand thepossibilities of case-based reasoning within the general WWW.

There are technological and methodological similarities betweenontologies and structured case-based reasoning and there aresynergies that can be reached by merging both approaches.

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CaseML - an RDF based Case Markup Language (by Chen and Wu);

CaseML offers a domain-independent case ontology and also aims to make case-based reasoning available within the Semantic Web.

SERVOGrid (by Aktas et al.) also uses RDF for caserepresentation;

It is embedded in a conversational case-based reasoning system that aids scientists in finding resources such as program code or data that are needed to solve a specific task by assisting them in describing the necessary resources using meta data.


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jCOLIBRI framework - OWL is being used as the case interchangelanguage;

It is planned to advance the already distributed framework towardsan architecture consisting of Semantic Web Services (SWS)where problem solving methods are represented as Web Services;

In order to use these services the whole case-based reasoning process is decomposed into single tasks, which are then carried out by according Web Services.


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There is a close relation between collaborative filtering and CBR and these two can benefit from each other.

Example 1: Collaborative filtering is used to assess the similaritybetween songs in a CBR system creating custom music compilations(CoCoA) [Aguzzoli et al.].

Example 2: A community based web search that uses the results ofprevious web searches of similar users in order to improve websearch results [Briggs and Smyth].

5. CBR and collaborative filtering

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During the past twenty years, many CBR applications have beendeveloped, ranging from prototypical applications build in researchlabs to large-scale fielded applications developed by commercialcompanies.

Application areas of CBR include:

- help-desk and customer service- recommender systems in electronic commerce- knowledge and experience management- medical applications and applications in image processing- applications in law, technical diagnosis, design, planning- applications in the computer games and music domain.

6. CBR applications

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We will compare CBR with the rule induction algorithmof machine learning.

Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem.

7. CBR compared to other methods

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The key difference, however, between the implicit generalization inCBR and the generalization in rule induction lies in when thegeneralization is made.

A rule-induction algorithm draws its generalizations from a set oftraining examples before the target problem is even known; that is, itperforms eager generalization.

This is in contrast to CBR, which delays (implicit) generalization ofits cases until testing time a strategy of lazy generalization.

CBR therefore tends to be a good approach for rich, complexdomains in which there are myriad ways to generalize a case.

7. CBR compared to other methods

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Critics of CBR argue that it is an approach that accepts anecdotal evidence as its main operating principle.

Without statistically relevant data for backing and implicitgeneralization, there is no guarantee that the generalization iscorrect.

There is recent work that develops CBR within a statistical framework and formalizes case-based inference as a specific type of probabilistic inference; thus, it becomes possible to produce case-based predictions equipped with a certain level of confidence.

8. Criticism of the CBR

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The number of CBR approaches and applications developed up tonow has become quite large.

There is a significant number of CBR research groups andcommercial companies, which develop CBR methods, softwarecomponents, and applications on a regular basis.

CBR is not only a technology but also a (process oriented) method.

The combination of CBR with various other technologies within agreat bandwidth of applications has become increasingly attractivefor researchers as well as business professionals.

9. Conclusion

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Ralph Bergmann, Klaus-Dieter Althoff, Mirjam Minor, Meike Reichle, Kerstin Bach:Case-Based Reasoning: Introduction and Recent Developments

Benjamin Heitmann, Conor Hayes:Enabling Case-Based Reasoning on the Web of Data

A. Aamodt, E. Plaza:Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches

10. References

Thank you for your attention!

Questions?

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Case Based Reasoning

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