Case-Based Reasoning

Case-Based Reasoning (CBR) A methodology in which knowledge and/or inferences are derived from historical cases Definition and concepts of cases in CBR Stories Cases with rich information and episodes. Lessons may be derived from this kind of cases in a case base

Case-Based Reasoning (CBR) Advantages of using CBR Knowledge acquisition is improved. System development time is faster Existing data and knowledge are leveraged Complete formalized domain knowledge is not required Experts feel better discussing concrete cases Explanation becomes easier Acquisition of new cases is easy Learning can occur from both successes and failures

Case-Based Reasoning (CBR)

CBR Logic Case-based reasoning is a problem solving paradigm that in many respects is fundamentally different from other major AI approaches. Instead of relying solely on general knowledge of a problem domain, or making associations along generalized relationships between problem descriptors and conclusions, CBR is able to utilize the specific knowledge of previously experienced, concrete problem situations (cases).

A new problem is solved by finding a similar past case, and reusing it in the new problem situation. A second important difference is that CBR also is an approach to incremental, sustained learning, since a new experience is retained each time a problem has been solved, making it immediately available for future problems. The CBR field has grown rapidly over the last few years, as seen by its increased share of papers at major conferences, available commercial tools, and successful applications in daily use.

4 step processes in CBR 1. Retrieve: Given a target problem, retrieve from memory cases relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case.

2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan.

4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his new-found procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands.

Comparison to other methods At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem.

Examples of CBR system SMART: Support management automated reasoning technology for Compaq customer service CoolAir: HVAC specification and pricing system Vidur - A CBR based intelligent advisory system, by C-DAC Mumbai, for farmers of North-East India. jCOLIBRI - A CBR framework that can be used to build other custom user-defined CBR systems. CAKE - Collaborative Agile Knowledge Engine. Edge Platform - Applies CBR to the healthcare, oil & gas and financial services sectors.