Introduction to CBR

 

Find a insightful (BOLD) CBR introduction online from dr. Thomas Roth-berghofer (http://www.dfki.de/web/research/km/expertise/research/case-based-reasoning? Set_language = en & CL = EN). The translation (with the original text) is as follows.

 

Case-Based Reasoning (CBR) is a problem-solving paradigm. its history dates back to the late 1970 s. CBR originated in cognitive science (often ascribed to Roger C. schank and Janet kolodner ). it can be explained in one simple sentence (also known as the CBR assumption ):

 

CBR is an example of problem solving. Its History can be traced back to the late 1970s S. It originated from cognitive science (typically thanks to Roger C. Schank and Janet kolodner ). CBR can be interpreted in one sentence (well-known CBR hypothesis ):

 

Similar problems have similar solutions.

 

Similar problems have similar solutions.

 

The basic idea of this assumption is to solve a current problem by reusing solutions that have been applied to similar problems in the past. therefore, the current problem has to be compared with problems described in cases. solutions contained in cases that represent very similar problems are then considered to be candidates for solving the current problem, too.

 

The basic idea of this assumption is to solve the current problem by reusing solutions that have been applied to similar problems in the past. Therefore, the current problem must be compared with the problem described in the case, and the solution included in the case that the problem is very similar is also considered as a candidate solution to the current problem.

 

As human beings we use this problem-solving technique in your situations in our daily routine. whenever it is easier or more convenient to re-use experience, humans prefer to do that rather than to derive solutions from scratch. the physician tells us that he saw the symptoms before, and one injection was all that was needed to heal that patient. the lawyer remembers a suitable case to defend our cause. and we hopefully remember recipes when we are cooking a new meal.

 

As humans, we use this problem solving technology in many situations in our daily lives. At any time, as long as it is easier or more convenient to repeat the experience, people will do this, rather than start-up export solutions. The doctor told us about the symptoms he saw before. Just take a needle to cure the patient. The lawyers think of a suitable case to defend against our reasons. When we make a new dish, we want to remember the previous recipe.

 

Case-Based Reasoning simulates this kind of human problem-solving behaviour. it shoshould be considered whenever it is difficult to formulate domain rules, and when cases are available. it shoshould also be when rules can be formulated but require more input information than is typically available, because of incomplete problem specifications or because the knodge DGE operation is simply not available at problem-solving time. other indications to use CBR are if general knowledge is not sufficient because of too provided exceptions, or when new solutions can be derived from old solutions more easily than from scratch. please successful your cial applications in these areas have proven the utility of this paradigm.

 

CBR imitates the problem solving behavior of humans. This method should be considered when it is difficult to formalize domain rules and use cases are available. This method should also be considered when rules can be formal but more typical available input information is required, because of incomplete Problem description or the knowledge required for solving the problem is not available. In other cases, it is easier to derive a new scheme from the old scheme to do so due to too much expectations and insufficient general knowledge. Many successful commercial software in these fields have proved the results of this example.

 

In order to enable a computer system to judge the similarity of two problems, CBR systems employ so-called "Similarity Measures" which represent a Mathematical Formalization of the very general term "similarity" or "utility ". similarity Measures usually do not describe the dependencies between problems and corresponding solutions in detail, but only represent a form of a heuristics.Thus, the selection of really useful cases, and therefore the strict correctness of the output, cannot be guaranteed in general. nevertheless, by tolerating this inexactness one is able to develop powerful knowledge-based systems with significantly less effort and lower costs as compared to the more traditional AI techniques that rely on a complete and correct domain theory.

 

To enable the computer system to determine the similarity between two problems, the CBR system uses the so-called "similarity measure", which indicates the mathematical form of the General Vocabulary "similarity" or "utility. Similarity measurements generally do not describe the dependencies between the problem and the corresponding solution in detail, but only represent heuristic experience.Therefore, it is generally not guaranteed that the output results are strictly correct when selecting useful cases. However, if we endure this inaccuracy, we can develop a powerful knowledge-based system. Compared with traditional AI technologies that rely on complete and correct domain theories, it only requires less effort and lower cost.

 

Case-Based Reasoning is not limited to the reuse of experience (e.g ., as it is used in help-desk applications), however. CBR is also very successful in Electronic Commerce scenarios and in product retrieval. here, similarity measures are used to compare user specifications with product descriptions, bridging the gap between customer demands and product features.

 

However, CBR is not limited to reuse of experience (for example, desktop help software). CBR is also very successful in e-commerce and product search. In this case, similarity measurements are used to compare user descriptions and product descriptions, and fill in the gap between customer requirements and product features.

 

Original: http://blog.csdn.net/xifeng_2008/archive/2008/10/11/3055716.aspx