Programming Project

CSE 335/435 - Intelligent Decision Support Systems - Fall 2006

Authors: Fabiana Prabhakar and

Shruti Bhandari

 

1)   Description of the Project

The ÒTour Planning Help SystemÓ is an Interactive CBR system for a tour planning domain. It essentially helps a user to decide a trip location depending on his personal preferences.

The user is able to choose pre-defined values among a list of attributes in order to create a new problem. The system will analyze this problem and show the cases in the case base order by its similarity to the problem.

The information gain metric provides the user extra information about the cases and can be very helpful during the attributes values selection.

The user can perform the following actions using the ÒTour Planning Help SystemÓ:

¤       read cases and facts files;

¤       assign/change values to attributes;

¤       see a case in detail;

¤       check the information gain values;

¤       enter weights for attributes;

¤       enter a parameter for matches/mismatches.


 

2)   The Interface

2.1)         Main Window

 

 


2.2)         Settings Window

 


2.3)         Facts Window

 


2.4)         Cases Window

 


3)   Formulae

3.1)         Information Gain

Remainder(A) =   p(A,1) I(p1/(p1+ n1), n1/(p1+ n1)) +

                                   p(A,2) I(p2/(p2+ n2), n2/(p2+ n2)) +É+

                                   p(A,v) I(pv/(pv+ nv), nv/(pv+ nv))

 

Gain(A) = I(p/p+n,n/p+n) – Remainder(A)

 

3.2)         Weighted Hamming distance with a parameter

¤       Matches

m = equal(X1,Y1) * α1 + equal(X2,Y2) * α2 + ... + equal(Xv,Yv) * αv;

¤       Mismatches

x = diff(X1,Y1) * α1 + diff(X2,Y2) * α2 + ... + diff(Xv,Yv) * αv;

¤       Hamming Distance

HW(X,Y) = 1- ((alpha*m)/((alpha*m) + ((1-alpha)*x)));

equal(A,B) { if (A==B) return 1;

                           else return 0; }

diff(A,B) { if (A!=B) return 1;

                           else return 0; }

¤       α1 + α2 + É + αv = 1      

¤       alpha is the weight of matches

 

4)   Initial Setup and Execution Guideline

4.1)         Known Cases file – caseBase2.xml


 

name

# of days

kind

religion

Month

climate

International

maximum_expense

solution

x1

>2 <5

family

 

Mar - Aug

hot

no

<1000

Rocky Mountain National Park, Colorado, US

x2

>2 <5

family

 

Sep - Feb

cold

no

<1000

Rocky Mountain National Park, Colorado, US

x3

>2 <5

family

 

Sep - Feb

cold

no

>1000 <2000

Disneyland, Orlando, US

x4

>2 <5

family

 

Mar - Aug

hot

no

>1000 <2000

Disneyland, Orlando, US

x5

>7

romantic

 

Mar - Aug

hot

no

>3000

Napa Valley, California, US

x6

>7

romantic

 

Sep - Feb

cold

no

>3000

Napa Valley, California, US

x7

>5 <7

romantic

 

Mar - Aug

hot

no

>1000 <2000

Napa Valley, California, US

x8

>5 <7

romantic

 

Sep - Feb

cold

no

>3000

Napa Valley, California, US

x9

>2 <5

urban

 

Mar - Aug

hot

no

<1000

San Francisco, California, US

x10

>2 <5

urban

 

Sep - Feb

cold

no

<1000

San Francisco, California, US

x11

>5 <7

urban

 

Mar - Aug

hot

no

>1000 <2000

San Francisco, California, US

x12

>5 <7

urban

 

Sep - Feb

cold

no

>1000 <2000

San Francisco, California, US

x13

>2 <5

urban

 

Mar - Aug

hot

no

<1000

New York, New York, US

x14

>2 <5

urban

 

Sep - Feb

cold

no

<1000

New York, New York, US

x15

>5 <7

urban

 

Mar - Aug

hot

no

>1000 <2000

New York, New York, US

x16

>5 <7

urban

 

Sep - Feb

cold

no

>1000 <2000

New York, New York, US

x17

>7

religious

catholic

Mar - Aug

hot

yes

>3000

Italy

x18

>7

religious

catholic

Sep - Feb

cold

yes

>3000

Italy

x19

>7

religious

catholic

Mar - Aug

hot

yes

>3000

Israel

x20

>7

religious

catholic

Sep - Feb

cold

yes

>3000

Israel

x21

>7

religious

jewish

Mar - Aug

hot

yes

>3000

Israel

x22

>7

religious

jewish

Sep - Feb

cold

yes

>3000

Israel

x23

>7

religious

hindu

Mar - Aug

hot

yes

>3000

India

x24

>7

religious

hindu

Sep - Feb

cold

yes

>3000

India