case-based reasoning (CBR)
Case-based reasoning (CBR) is an experience-based approach to solving new problems by adapting previously successful solutions to similar problems. Addressing memory, learning, planning and problem solving, CBR provides a foundation for a new technology of intelligent computer systems that can solve problems and adapt to new situations. In CBR, the “intelligent” reuse of knowledge from already-solved problems, or cases, relies on the premise that the more similar two problems are, the more similar their solutions will be.
CBR developed its roots in the work of artificial intelligence theorist and cognitive psychologist, Roger Schank, and his students at Yale in the late 20th century. The researchers studied the problem-solving ability of humans and found that most people assemble solutions based on earlier experiences with similar situations.
With applications spanning fields ranging from machine learning to medicine to law, CBR is accomplished by gathering case histories and implemented by identifying significant features that describe a case. CBR systems can “learn” by acquiring new knowledge as cases. This, along with the application of database techniques, makes it easier to maintain large volumes of information.
Four step process for CBR
In general, the case-based reasoning process entails:
- Retrieve- Gathering from memory an experience closest to the current problem.
- Reuse- Suggesting a solution based on the experience and adapting it to meet the demands of the new situation.
- Revise- Evaluating the use of the solution in the new context.
- Retain- Storing this new problem-solving method in the memory system.
Comparison to other techniques
Case-based reasoning has several differences from other AI approaches, such as knowledge-based systems (KBS). Rather than relying completely on general knowledge of a problem domain or making associations along generalized relationships between problem descriptors and conclusions, CBR employs the specific knowledge of previously experienced, concrete problem situations. CBR also offers incremental, sustained learning in that each time a problem is solved a new experience is retained and can be applied for future problems.
Advantages and disadvantages of CBR
Scientists cite both advantages and disadvantages to CBR. On the plus side, remembering past experiences helps learners avoid repeating previous mistakes, and the reasoner can discern what features of a problem are significant and focus on them.
Further, CBR is intuitive because it reflects how people work. Because no knowledge must be elicited to create rules or methods, development is easier. Another benefit is that systems learn by acquiring new cases through use, and this makes maintenance easier.
CBR also enables the reasoner to propose solutions to problems quickly. The reasoner can propose solutions in areas that he or she does not fully understand, evaluate solutions when no algorithmic method is available and interpret open-ended and ill-defined concepts.
On the negative side, critics claim that the main premise of CBR is based on anecdotal evidence and that adapting the elements of one case to another may be complex and potentially lead to inaccuracies. However, recent work has enhanced CBR by using a statistical framework. This makes it possible to produce case-based predictions with a higher level of confidence.