AIAI Case Based Reasoning

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In case-based reasoning (CBR) systems expertise is embodied in a library of past cases, rather than being encoded in classical rules. Each case typically contains a description of the problem, plus a solution and/or the outcome. The knowledge and reasoning process used by an expert to solve the problem is not recorded, but is implicit in the solution.

To solve a current problem: the problem is matched against the cases in the case base, and similar cases are retrieved. The retrieved cases are used to suggest a solution which is reused and tested for success. If necessary, the solution is then revised. Finally the current problem and the final solution are retained as part of a new case.

Case-based reasoning is liked by many people because they feel happier with examples rather than conclusions separated from their context. A case library can also be a powerful corporate resource, allowing everyone in an organisation to tap into the corporate case library when handling a new problem.

Since the 1990's CBR has grown into a field of widespread interest, both from an academic and a commercial standpoint. Mature tools and application-focused conferences exist. Case-based reasoning is often used as a generic term to describe techniques including but not limited to case-based reasoning as we describe it here (e.g. analogical reasoning is often referred to as case-based reasoning).


All case-based reasoning methods have in common the following process:

There are a variety of different methods for organising, retrieving, utilising and indexing the knowledge retained in past cases.

Retrieving a case starts with a (possibly partial) problem description and ends when a best matching case has been found. The subtasks involve:

Some systems retrieve cases based largely on superficial syntactic similarities among problem descriptors, while advanced systems use semantic similarities.

Reusing the retrieved case solution in the context of the new case focuses on: identifying the differences between the retrieved and the current case; and identifying the part of a retrieved case which can be transferred to the new case. Generally the solution of the retrieved case is transferred to the new case directly as its solution case.

Revising the case solution generated by the reuse process is necessary when the solution proves incorrect. This provides an opportunity to learn from failure.

Retaining the case is the process of incorporating whatever is useful from the new case into the case library. This involves deciding what information to retain and in what form to retain it; how to index the case for future retrieval; and integrating the new case into the case library.

A CBR tool should support the four main processes of CBR: retrieval, reuse, revision and retention. A good tool should support a variety of retrieval mechanisms and allow them to be mixed when necessary. In addition, the tool should be able to handle large case libraries with retrieval time increasing linearly (at worst) with the number of cases.


Case based reasoning first appeared in commercial tools in the early 1990's and since then has been sued to create numerous applications in a wide range of domains:

A list of applications can be found here.

A book by Ian Watson has been published called "Applying Case Based Reasoning: Techniques for Enterprise Systems". Further information and a detailed list of contents is available.


Some of the characteristics of a domain that indicate that a CBR approach might be suitable include:

  1. records of previously solved problems exist;
  2. historical cases are viewed as an asset which ought to be preserved;
  3. remembering previous experiences is useful;
  4. specialists talk about their domain by giving examples;
  5. experience is at least as valuable as textbook knowledge.

Case-based reasoning is often used where experts find it hard to articulate their thought processes when solving problems. This is because knowledge acquisition for a classical KBS would be extremely difficult in such domains, and is likely to produce incomplete or inaccurate results. When using case-based reasoning, the need for knowledge acquisition can be limited to establishing how to characterise cases.

Case-based reasoning allows the case-base to be developed incrementally, while maintenance of the case library is relatively easy and can be carried out by domain experts.


A list of CBR development tools, both commercial and academic can be found here

A comprehensive review of CBR software tools is also available.

Another review paper in Word format is also available.


Below are sites that include many references and links to electronic CBR resources:

  1. US Navy Research Website
  2. University of Kaiserlautern CBR website
  3. AI-CBR: Salford University Website

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Last updated 30th May 1997
by Ian Harrison