Case-Based Reasoning

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Case-Based Reasoning
Faculty of Electrical Engineering
University of Belgrade
Davitkov Miroslav, 2011/3116
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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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1. Case-Based Reasoning definition
Everyday examples of CBR

• 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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2. CBR problem solver

1. Case previously made and stored experience item

• Case-Base core of every case based problem
  solver
• - collection of cases

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2. CBR problem solver

• 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.

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

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2. CBR problem solver

• 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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3. Types of CBR
There are three main types of CBR that differ
significantly from one another concerning case
representation and reasoning

• Structural (a common structured vocabulary, i.e.
  an ontology)
• Textual (cases are represented as free text, i.e.
  strings)
• Conversational
• (a case is represented through a list of
  questions that varies from one case to another
  knowledge is contained in customer / agent
  conversations)

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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 is proposed by Aamodt and Plaza.

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

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4. CBR Cycle
Problem
New Case
RETRIEVE
Learned Case
General Knowledge
Retrieved Case
New Case
RETAIN
Tested / Repaired Case
REUSE
Solved Case
REVISE
Suggested Solution
Confirmed Solution
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4. CBR Cycle
4.1. Retrieve

• 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 gt methods that improve
  retrieval efficiency, e.g. specific index
  structures such as kd-trees, case-retrieval nets,
  or discrimination networks.

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4. CBR Cycle
4.2. Reuse

• 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.

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4. CBR Cycle
4.3. Revise

• 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.

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4. CBR Cycle
4.4. Retain

• 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.

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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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5. CBR and the Future Internet

• 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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5. CBR and the Future Internet

• 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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5. CBR and the Future Internet

• 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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5. CBR and collaborative filtering

• 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.

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6. CBR applications

• 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.

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7. CBR compared to other methods

• 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.

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7. CBR compared to other methods

• 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.

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8. Criticism of the CBR

• 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.

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9. Conclusion

• 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.

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10. References

• 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

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Thank you for your attention!
Questions?
davitkov.miroslav_at_gmail.com dm113116m_at_student.etf.
rs