Knowledge Learning by Using Case Based Reasoning (CBR)

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Presentation transcript:

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 4/24/2017

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 4/24/2017

CBR System Components Case-base Retrieval of relevant cases 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) 4/24/2017

The Case Based Reasoning Cycle 4/24/2017 The Case Based Reasoning Cycle

Case Retrieval and Adaptation 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. 4/24/2017

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) 4/24/2017

freeCBR a very small case set: 4/24/2017

freeCBR (cont.) search from the case set: the result of the search: 4/24/2017

Open Tool – myCBR 4/24/2017

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. 4/24/2017

Case Retrieval Retrieve most similar k-nearest neighbor - k-NN Nearest Neighbor Retrieval Retrieve most similar k-nearest neighbor - k-NN - like scoring in bowls or curling Example 1-NN 5-NN 4/24/2017

Case Retrieval Case-Base indexed using a decision-tree Decision Tree e.g. Case-Base indexed using a decision-tree 4/24/2017

Case Retrieval We propose a self-organizing reservoir computing based network for case retrieval. 4/24/2017

, 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 : 4/24/2017

NARMA task: Mean squared error = 0.128221, std = 0.0200301 4/24/2017

Future Work Integrate RC based network into CBR system Develop the CBR system based on existing tools for more complicated tasks 4/24/2017