Case-based reasoning

What is case-based reasoning?

The idea of case-based reasoning (cbr) comes from the psychological model that people can react to similar problems with experiences they have had, from tasks they have been given. With the help of these analogies machine learning Problem solutions generated and applied.

Case-based reasoning captures machine experience through a problem and a matching solution. This is the general model of a CBR system, in which cases from the past are used to solve current problems. Experience is the basis for building even more knowledge-based systems, but knowledge is used differently in CBR systems, so these applications are seen as a further development of knowledge-based systems.

How does a CBR cycle work?

Case-based reasoning is a well-founded paradigm of the Artificial Intelligence (AI) for problem solving based on experience. This is based on the observation that similar problems usually require similar solutions. Accordingly, the idea of case-based reasoning is to use knowledge gained from solving problems in the past to adapt similar problems to the current situation. For this purpose, the solutions of the former problems are adapted in order to be able to transfer them to the new problem.

The The main component of any case-based problem solver is the case base. This is a collection of stored units of experience, the cases. Such a case contains a description of the problem and a corresponding solution. As a rule, the case basis is in the Database stored and forms the basic knowledge of the problem solver.

The new problems are solved by retrieving cases from a case base that are analogous to the current problem. The experiences stored in the similar cases are then reused. For example, parts of a solution can be adapted to a new problem and possibly combined.

What are applications for case-based reasoning?

The Case-based reasoning has proven itself especially in application systems for customer service, the help desk systems.where the user uses it, for example, to diagnose customer enquiries. Recently, it has been increasingly used in Consultancy systems used for products, for example in e-commerce and for structuring texts.

The advantage is that the method can be used even for incompletely described and poorly structured problems. Compared to neighbouring concepts, a rather small collection of references is sufficient at the beginning, which grows further and further by working with the CBR system. CBR is also suitable for application domains whose interdependencies are not always fully known.

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