knowledge learning by using case based reasoning (cbr)

Knowledge Learning by Using Case Based Reasoning (CBR)

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Jun Yin and Yan MengDepartment of Electrical and Computer Engineering

Stevens Institute of TechnologyHoboken, NJ, USA


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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


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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)


Problem

RETRIEVE

SIMILAR CASES

PRIOR CASES

CASE-BASE

REUSE

Solution

New case

REVISESolution

RETAIN

The Case Based Reasoning Cycle


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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.


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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)


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freeCBR

a very small case set:


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the result of the search:

freeCBR (cont.)search from the case set:


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Open Tool – myCBR


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Open Tools – freeCBR & myCBRModeling 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.

n

iiiii cqsimcqSim

1

),(),(


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Case Retrieval• Nearest Neighbor Retrieval

Retrieve most similar k-nearest neighbor

- k-NN- like scoring in bowls or curling

Example- 1-NN- 5-NN


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Case-Base indexedusing a decision-tree

Case Retrieval• Decision Tree

e.g.


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Case Retrieval

Self-organizing Reservoir Computing based Network architecture

Self-Organizing Topology with SNN

x(t)

y(t)

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

C a s e

B a s e Case Retriecal

Self-organizing RC based network

Query

Previous Cases

q

The Most Similar Case


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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 :

1.0)()9(5.1])()[(05.0)(3.0)1( 9

0

tutuitytytytyi

,


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10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

# 1

number

valu

es

expected valuesestimated values

10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

# 2

number

valu

es

expected valuesestimated values

Mean squared error = 0.128221, std = 0.0200301

NARMA task:


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Future Work• Integrate RC based network into CBR system

• Develop the CBR system based on existing tools for more complicated tasks

knowledge learning by using case based reasoning (cbr)

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