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 that exhibited similar symptoms

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

• An engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems.

• 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 that similar problems have similar solutions.

• A case-based problem solver solves new problems primarily by 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 significantly from one another concerning case representation and reasoning:

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4. CBR Cycle

• Despite the many different appearances of CBR systems, the essentials of CBR are captured in a surprisingly simple and uniform process model.

• The CBR cycle consists of 4 sequential steps around the knowledge 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

Learned Case

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 from the case-base with the highest similarity to the query.

• This is a k-nearest-neighbor retrieval task considering a specific similarity 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 is returned 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 extensive adaptation for pragmatic reasons.

4.2. Reuse

4. CBR Cycle

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• In this phase, feedback related to the solution constructed so far is obtained.

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

• The revised case or any other form of feedback enters the CBR system 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 a revised case to the case base).

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

• The revised case or any other form of feedback enters the CBR system 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 major factors: 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

Ontology Language), which is based on RDF

• Already before the development of RDF and OWL, XML has been used as a case representation within the case-based reasoning community.

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• There is a notable similarity between the ontologies developed within semantic applications and the representation of cases in structural case-based reasoning.

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

• There are technological and methodological similarities between ontologies and structured case-based reasoning and there are synergies 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 case representation;

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 interchange language;

It is planned to advance the already distributed framework towards an 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 similarity between songs in a CBR system creating custom music compilations (CoCoA) [Aguzzoli et al.].

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

5. CBR and collaborative filtering

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• During the past twenty years, many CBR applications have been developed, ranging from prototypical applications build in research labs to large-scale fielded applications developed by commercial companies.

• 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 algorithm of 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 in CBR and the generalization in rule induction lies in when the generalization is made.

• A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization.

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

• CBR therefore tends to be a good approach for rich, complex domains 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 implicit generalization, there is no guarantee that the generalization is correct.

• 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 to now has become quite large.

• There is a significant number of CBR research groups and commercial companies, which develop CBR methods, software components, 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 a great bandwidth of applications has become increasingly attractive for 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