Transcript Case-Based Reasoning

Case-Based Reasoning
Case-Based Reasoning (CBR)
• Case-based reasoning (CBR)
A methodology in which knowledge and
/or inferences are derived from historical
cases
• Definition and concepts of cases in CB
R
– Stories
Cases with rich information and episodes. Le
ssons may be derived from this kind of case
s in a case base
Case-based reasoning
Case-based reasoning (CBR), broadly
construed, is the process of solving new
problems based on the solutions of
similar past problems. An auto mechanic
who fixes an engine by recalling another
car that exhibited similar symptoms is
using case-based reasoning. A lawyer who
advocates a particular outcome in a trial
based on legal precedents or a judge who
creates case law is using case-based
reasoning.
It has been argued that case-based
reasoning is not only a powerful method
for computer reasoning, but also a
pervasive behavior in everyday human
problem solving; or, more radically, that
all reasoning is based on past cases
personally experienced. This view is
related to prototype theory, which is
most deeply explored in cognitive
science.
Case-Based Reasoning (CBR)
Case-Based Reasoning (CBR)
• Benefits and usability of CBR
– CBR makes learning much easier and the rec
ommendation more sensible
Case-Based Reasoning (CBR)
• Advantages of using CBR
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Knowledge acquisition is improved.
System development time is faster
Existing data and knowledge are leveraged
Complete formalized domain knowledge is not requi
red
Experts feel better discussing concrete cases
Explanation becomes easier
Acquisition of new cases is easy
Learning can occur from both successes and failures
Case-Based Reasoning (CBR)
CBR solves problems using the already
stored knowledge, and captures new
knowledge, making it immediately
available for solving the next problem.
Therefore, case-based reasoning can be
seen as a method for problem solving,
and also as a method to capture new
experience and make it immediately
available for problem solving.
It can be seen as a learning and knowledgediscovery approach, since it can capture
from new experience some general
knowledge, such as case classes, prototypes
and some higher-level concept. The idea of
case-based reasoning originally came from
the cognitive science community which
discovered that people are rather reasoning
on formerly successfully solved cases than
on general rules.
The case-based reasoning community
aims to develop computer models that
follow this cognitive process. For many
application areas computer models have
been successfully developed, which were
based on CBR, such as signal/image
processing and interpretation tasks, helpdesk applications, medical applications
and E-commerce product-selling systems.
In the tutorial we will explain the casebased reasoning process scheme. We will
show what kind of methods are
necessary to provide all the functions for
such a computer model. We will develop
the bridge between CBR and other
disciplines. Examples will be given based
on signal-interpreting applications and
information management.
Case-based reasoning is a problem solving
paradigm that in many respects is
fundamentally different from other major AI
approaches. Instead of relying solely on
general knowledge of a problem domain, or
making associations along generalized
relationships between problem descriptors
and conclusions, CBR is able to utilize the
specific knowledge of previously
experienced, concrete problem situations
(cases).
A new problem is solved by finding a similar past
case, and reusing it in the new problem situation.
A second important difference is that CBR also is
an approach to incremental, sustained learning,
since a new experience is retained each time a
problem has been solved, making it immediately
available for future problems. The CBR field has
grown rapidly over the last few years, as seen by
its increased share of papers at major
conferences, available commercial tools, and
successful applications in daily use.
4 step processes in CBR
1. Retrieve: Given a target problem, retrieve
from memory cases relevant to solving it. A
case consists of a problem, its solution, and,
typically, annotations about how the
solution was derived.
For example, suppose Fred wants to
prepare blueberry pancakes. Being a novice
cook, the most relevant experience he can
recall is one in which he successfully made
plain pancakes. The procedure he followed
for making the plain pancakes, together
with justifications for decisions made along
the way, constitutes Fred's retrieved case.
2. Reuse: Map the solution from the previous case
to the target problem. This may involve adapting
the solution as needed to fit the new situation. In
the pancake example, Fred must adapt his
retrieved solution to include the addition of
blueberries.
3. Revise: Having mapped the previous solution to
the target situation, test the new solution in the
real world (or a simulation) and, if necessary,
revise. Suppose Fred adapted his pancake
solution by adding blueberries to the batter. After
mixing, he discovers that the batter has turned
blue – an undesired effect. This suggests the
following revision: delay the addition of
blueberries until after the batter has been ladled
into the pan.
4. Retain: After the solution has been
successfully adapted to the target
problem, store the resulting experience
as a new case in memory. Fred,
accordingly, records his new-found
procedure for making blueberry
pancakes, thereby enriching his set of
stored experiences, and better preparing
him for future pancake-making demands.
Comparison to other methods
At first glance, CBR may seem similar to
the rule induction algorithms 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.
Prominent CBR systems
• SMART: Support management automated
reasoning technology for Compaq customer
service
• CoolAir: HVAC specification and pricing system
• Vidur - A CBR based intelligent advisory system,
by C-DAC Mumbai, for farmers of North-East
India.
• jCOLIBRI - A CBR framework that can be used to
build other custom user-defined CBR systems.
• CAKE - Collaborative Agile Knowledge Engine.
• Edge Platform - Applies CBR to the healthcare,
oil & gas and financial services sectors.