ABSTRACT
Artificial
Intelligence is all about bringing Common Sense, Expert Knowledge, and
Superhuman Reasoning to Computers. For the most part, AI does not produce
stand-alone systems, but instead adds knowledge and reasoning to existing
applications, databases, and environments, to make them friendlier, smarter,
and more sensitive to user behavior and changes in their environments.
In the domain of
Artificial intelligence, various problem-solving techniques have been
developed. Though working towards the common goal of making a computer
'intelligent', all these techniques use different methodologies. Case Based
Reasoning is one of these techniques. Computer systems that solve new
problems by analogy with old ones are often called Case Based Reasoning (CBR)
systems.
This paper answers
fundamental questions like what is CBR and how is it related to human
reasoning, the different issues involved in developing a CBR system and CBR's
comparison with other problem-solving techniques.
Introduction
Case Based
Reasoning (CBR)
is a powerful technique to search and retrieve information from a collection of
past experiences (cases). These technologies enable preserving and sharing best
practices in service and diagnostic.
Consider a simple
example of Case Based Reasoning (CBR) that deals with car diagnostics. A
case stored in the case base is a fault that has been solved in the past. The
case description is made up of effects, such as observed symptoms (e.g., engine
does not start) and context parameters (e.g., ignition key is turned on). It
can also include measured parameters for example, the state of the electronic
control units obtained using testing equipment. The solution is the maintenance
operation.
With CBR, you can
make use of the experience captured in this case base to solve new diagnostic
problems. If you encounter a new, unsolved diagnostic problem, a past case that
is similar to your new problem will very likely contain an appropriate
maintenance operation.
Analogy to human reasoning
When confronted
with a new problem, a technician with no or little experience may attempt to
analyze the problem using a Fault Isolation Manual, if there is one and if this
is not an overly time-consuming task. He might also try to find the source of
the problem by himself, in which case he may end up changing the wrong parts.
Finally, he might ask for help, either by calling the car manufacturers
technical support center or by asking a more experienced colleague.
A more-experienced mechanic can recall past cases he has solved. His intuitive thinking process is, "Have I ever seen a similar problem before? If so, what did I do to solve it?" If the more-experienced mechanic can find the solution and fix the car, his less-experienced colleague will learn from this new experience and build up his own memory of solved cases. This human ability to learn is a key to human intelligence and reasoning.
If the experience
of its employees is indeed a valuable asset to a company, it makes sense to try
to capture this experience and store it in such a way that it can be reused in
the future and shared among the company's individuals.
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