Case Based Reasoning

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).

Technical

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

retrieve the most similar case (or cases) comparing the case to the library of past cases;
reuse the retrieved case to try to solve the current problem;
revise and adapt the proposed solution if necessary;
retain the final solution as part of a new case.

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:

identifying a set of relevant problem descriptors;
matching the case and returning a set of sufficiently similar cases (given a similarity threshold of some kind); and
selecting the best case from the set of cases returned.

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.

Applications

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:

Diagnosis: case-based diagnosis systems try to retrieve past cases whose symptom lists are similar in nature to that of the new case and suggest diagnoses based on the best matching retrieved cases. The majority of installed systems are of this type and there are many medical CBR diagnostic systems.
Help Desk: case-based diagnostic systems are used in the customer service area dealing with handling problems with a product or service.
Assessment: case-based systems are used to determine values for variables by comparing it to the known value of something similar. Assessment tasks are quite common in the finance and marketing domains.
Decision support: in decision making, when faced with a complex problem, people often look for analogous problems for possible solutions. CBR systems have been developed to support in this problem retrieval process (often at the level of document retrieval) to find relevant similar problems. CBR is particularly good at querying structured, modular and non-homogeneous documents.
Design: Systems to support human designers in architectural and industrial design have been developed. These systems assist the user in only one part of the design process, that of retrieving past cases, and would need to be combined with other forms of reasoning to support the full design process.

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.

Suitability

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

records of previously solved problems exist;
historical cases are viewed as an asset which ought to be preserved;
remembering previous experiences is useful;
specialists talk about their domain by giving examples;
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.

Tools

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.

Websites

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

Mailing Lists

AI-CBR. A mailing list including announcements, questions and discussion about CBR, managed by Ian Watson and Farhi Marir at Salford University. To join send an electronic mail message to mailbase@mailbase.ac.uk with "join ai-cbr " as the body of the message.
CBR Newsletter. A quarterly electronic newsletter that originated as a publication of the Special Interest Group on Case-Based Reasoning in the German Society for Computer Science. It is managed by Dietmar Janetzko of the University of Freiburg and Stefan Wess of Inference Corporation. The home page for the newsletter is here.

Last updated 30th May 1997
by Ian Harrison