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Knowledge Learning by Using Case Based Reasoning (CBR)

Knowledge Learning by Using Case Based Reasoning (CBR). Jun Yin and Yan Meng Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, USA. What’s CBR?.

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Knowledge Learning by Using Case Based Reasoning (CBR)

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  1. Knowledge Learning by Using Case Based Reasoning (CBR) Jun Yin and Yan Meng Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, USA

  2. What’s CBR? • Case-Based Reasoning (CBR) is a name given to a reasoning method that solves a new problem by remembering a previous similar experiences and by reusing information and knowledge of that situation. • Ex: Medicine • doctor remembers previous patients especially for rare combinations of symptoms • Ex: Law • English/US law depends on precedence • case histories are consulted

  3. CBR System Components • Case-base • database of previous cases (experience) • Retrieval of relevant cases • matching most similar case(s) • retrieving the solution(s) from these case(s) • Adaptation of solution • alter the retrieved solution(s) to reflect differences between new case and retrieved case(s)

  4. The Case Based Reasoning Cycle

  5. Case Retrieval and Adaptation • Case retrieval • the process of finding within the case base those cases that are the closest to the current case. • Nearest Neighbor Retrieval • Inductive approaches • Knowledge Guided Approaches • Validated Retrieval • Case Adaptation • the process of translating the retrieved solution into the solution appropriate for the current problem.

  6. Open Tools • freeCBR • is a free open source Java implementation of a "Case Based Reasoning" engine. (http://freecbr.sourceforge.net/) • myCBR • is an open-source case-based reasoning tool developed at DFKI. (http://mycbr-project.net/index.html)

  7. freeCBR a very small case set:

  8. freeCBR (cont.) search from the case set: the result of the search:

  9. Open Tool – myCBR

  10. Open Tools – freeCBR& myCBR Modeling Similarity Measures: These two tools follow the approach in which, for an attribute-value based case representation consisting of n attributes, the similarity between a query q and a case c may be calculated as follows: Here, simi and wi denote the local similarity measure and the weight of attribute i, and Sim represents the global similarity measure.

  11. Case Retrieval • Nearest Neighbor Retrieval • Retrieve most similar • k-nearest neighbor • - k-NN • - like scoring in bowls or curling • Example • 1-NN • 5-NN

  12. Case Retrieval • Decision Tree • e.g. Case-Base indexedusing a decision-tree

  13. Case Retrieval • We propose a self-organizing reservoir computing based network for case retrieval.

  14. , Case Retrieval • Benchmark to evaluate the performance of proposed RC based network. • NARMA task • - The Nonlinear Auto-Regressive Moving Average (NARMA) task consists of modeling the output of the following tenth-order system :

  15. NARMA task: Mean squared error = 0.128221, std = 0.0200301

  16. Future Work • Integrate RC based network into CBR system • Develop the CBR system based on existing tools for more complicated tasks

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