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|Title: ||Case Based Reasoning System|
|Authors: ||Rainu Boben|
|Keywords: ||Reasoning, Case base, Dynamic memory, Machine learning|
|Issue Date: ||22-Jun-2011|
|Abstract: ||Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning. Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Casebased reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation or create an equitable solution to a new problem.
The roots of case-based reasoning in AI is found in the works of Roger Schank on dynamic memory. The first system that might be called a case-based reasoner was the CYRUS system, developed by Janet Kolodner, at Yale University. CYRUS was based on Schank's dynamic memory model of problem solving and learning. Case-based reasoning (CBR) puts forward a paradigmatic way to attack AI issues, namely problem solving, learning, usage of general and specific knowledge, combining different reasoning methods, etc. In particular CBR emphasizes problem solving and learning as two sides of the same coin: problem solving uses the results of past learning episodes while problem solving provides the backbone of the experience from which learning advances.|
|Appears in Collections:||MTech 2010-2012 Batch|
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