Faculty of Electrical Engineering
University of Belgrade
Case-Based Reasoning
Davitkov Miroslav, 2011/3116
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.
2 / 25
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.
3 / 25
2. CBR problem solver
1. Case – previously made and stored experience item
2. Case-Base – core of every case – based problem solver
- collection of cases
4 / 25
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.
5 / 25
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.
6 / 25
3. Types of CBR
There are three main types of CBR that differ significantly
from one another concerning case representation and
reasoning:
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)
7 / 25
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.
8 / 25
4. CBR Cycle
Problem
New Case
RETRIEVE
Learned
Case
Retrieved Case
Previous
Cases
RETAIN
New Case
General Knowledge
Tested /
Repaired
Case
Confirmed
Solution
REUSE
REVISE
Solved Case
9 / 25
Suggested
Solution
4. CBR Cycle
4.1. Retrieve
• One or several cases from the
based on the modeled similarity.
case base
are selected,
• 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.
10 / 25
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.
11 / 25
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.
12 / 25
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.
13 / 25
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.
14 / 25
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.
15 / 25
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.
16 / 25
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.
17 / 25
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].
18 / 25
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.
19 / 25
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.
20 / 25
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.
21 / 25
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.
22 / 25
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.
23 / 25
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
24 / 25
Thank you for your attention!
Questions?
davitkov.miroslav@gmail.com
dm113116m@student.etf.rs