Case Based Reasoning Examples

Case-based reasoning

Case-based reasoning is a problem solving paradigm that in many respects is fundamentally different from other major AI approaches. Instead of re-lying 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 case library has cases covering a broad range of ideas across different industries and business functions. Each case contains a description of the underlying competitive situation, the environmental conditions, management priorities, experience, values that allow a certain strategy to succeed, and moments of learning. A software system helps index each case such a way that a search yields modest number of similar cases. The system can provide a complete explanation if the reasoning that has led to each recommendation. If there is no case that exactly matches the given situation, then it selects the most similar case. An adaptation procedure can be encoded in the form of adaptation rules. The result of the case adaptation is a completed solution but it also generates a new case that can be automatically added to the case

(1996b)

Knowledge discovery Process Model: KD process model (source: http://www.crisp-dm.org/)

Complete definition of all Steps involved in the Knowledge Discovery Process Model:

1. Business understanding - This step focuses on the understanding of objectives and requirements from a business perspective. It also converts these into a DM problem definition, and designs a preliminary project plan to achieve the objectives (Fayyad et al. (1996b))

• It is further broken into several sub steps namely:

- determination of business objectives

- assessment of the situation

- determination of DM goals

- generation of a project plan

2. Data understanding - This step starts with initial data collection and familiarization with the data. Specific aims include identification of data quality problems, initial insights into the data, and detection of interesting data subsets. Data understanding is further broken down into several Steps as well such as:

- collection of initial data

- description of data

- exploration of data

- verification of data