Knowledge Learning by Using Case Based Reasoning (CBR)

Knowledge Learning by Using Case Based Reasoning (CBR)

04/22/23 1


Jun Yin and Yan MengDepartment of Electrical and Computer Engineering

Stevens Institute of TechnologyHoboken, NJ, USA


04/22/23 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


04/22/23 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)


Problem

RETRIEVE

SIMILAR CASES

PRIOR CASES

CASE-BASE

REUSE

Solution

New case

REVISESolution

RETAIN

The Case Based Reasoning Cycle


04/22/23 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.


04/22/23 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)


04/22/23 7

freeCBR

a very small case set:


04/22/23 8

the result of the search:

freeCBR (cont.)search from the case set:


04/22/23 9

Open Tool – myCBR


04/22/23 10

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

),(),(


04/22/23 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


04/22/23 12

Case-Base indexedusing a decision-tree

Case Retrieval• Decision Tree

e.g.


04/22/23 13

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


04/22/23 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 :

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

0

tutuitytytytyi

,


04/22/23 15

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:


04/22/23 16

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)

Documents

Transcript of Knowledge Learning by Using Case Based Reasoning (CBR)