Intelligent Information Expert System for Employment

and General Purposed Fuzzy Shell

2

Technological centre:

Solware Information Technology Ltd.Tasks:

• coordinator• project leader• software development

Knowledge centre:

Budapest University of Technology and Economics, Department of Telecommunication and Telematics Tasks:

• fuzzy algorithms• aggregation

- general searching-offering system

- general fuzzy shell

Members of the Consortium

3

• Project goals

• Background

- expert systems

- fuzzy definitions

- fuzzy rule-based expert systems (fuzzy shell)

- comparison• Project

- fuzzy based searching-offering systems

- employment expert system• accomplishment with modern IT methods• design, optimisation system parameters

Conclusions

Agenda

4

Goal: Development of a specific and a general purposed fuzzy rule-based expert system

Two steps:

1. Development of a fuzzy-based searching-offering system2. Development of a general purposed fuzzy-shell

Mile stones:a. Fuzzy-based searching-offering subsystemb. Job searching subsystemc. Applicant searching subsystemd. Fuzzy shelle. Optimisation the parameters of employment (job and

applicant searching) f. Design fuzzy-shell demo program

Goal

Project Goals, Tasks

5

Knowledge base

Antecedence Consequence

Conclusion

Character of the knowledge base:

The rules are applied to crisp values and intervals

Difficulties:

• very large knowledge base, too many rules

• the uncertainties are handled not efficiently

• inflexible system: no applicable rule no result

Backgr

.Expert Systems

6

• Fuzzy setA is a set on the X universe,

Fuzzy set: belongs to the given A set so that the measure of this membership is not 1 or 0 (x belongs to A or not) but a value between the two

• Membership function

The measure of the belonging

• Fuzzy logicGeneralisation of the two-valued Boole type logic

income

(USD)

Backgr

.

1,0Xx

A

1

0

A

3000 USD

A=more then 3000 USD

Fuzzy Definitions

XA

Xx

7

Observation Crisp output

Rule-base

Backgr

.

Fuzzyfication

unit

Defuzzyfication unit

Inference engine

Inference Algorithm i.e.

Mamdani Sugeno, etc..

If Then

Fuzzy Rule-Based Expert Systems (Fuzzy Shell)

8

Advantage over the classical expert systems:

• less rule • decrease computational complexity

c = decreasing factor against symbolical expert systems

• good handling the uncertainties• robust system (overlapping rules)

Disadvantage:• decreasing accuracy

Applications have great perspectives on the areas where the uncertainty is large and not needed very accurate result

Additional new components comparing to other (fuzzy) expert systems:

• build in interpolative methods

• hierarchical systems

Backgr

.

kk

cTT

Comparison

9

Problems by finding the partners each other:

- in discrete case:search

offer

Similarity

matrix

Projec

t

1

0

Offer Search

0.45

A solution: using fuzzy sets- in continuos case:

• uncertainties:

the searching partner

doesn’t know exactly

what he/she wants

• weight of viewpoints

are differences and can be

changing during the process

Fuzzy Based Searching-Offering Systems

10

• Variables were chosen and structured by professional employment experts

• Typical variable groups- income (salary and other)

- personal skills (education, language etc.)- workplace information (distance, firm size)

• More problematical case were handled:

- distance: - taking into account the infrastructure the system able to calculate the distance in time

- branches: - all the branches are covered by similarity matrix

- weight: - the weights of the variables can be iterated after the analysis of the output

Projec

tEmployment Expert System

11

Algorithms

- user surface on web and windows environment

- SQL database

- XML, MTS applications

Projec

t

Job searchinguser surface Employment

agency surface

Job offering user surface

User surfaces

DatabaseApplication logic

Fuzzy system

Accomplishment

12

Architecture of Employment Expert SystemProjec

t

Internet client

Internet client

Internet client

Web server Application server

XML configuration

files

SQL data base

13

• optimisation on real data

• the learning algorithm is a type of evolutionary algorithm: bacterial algorithm

Optimisation with learning methods

Check the results on test set

Projec

t

Default parameters(employment experts)

Choose the parameters for optimisation

Design, Optimisation of System Parameters

14

Summ

a

• The method- Advantages and disadvantages of fuzzy-based expert systems

- Motivation of using fuzzy methods

• Until nowGeneral searching-offering system

right now: Job searching subsystem

• Future - Applicant searching subsystem

- General purposes fuzzy shell

Conclusions