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This project aims to develop a fuzzy-based searching-offering system and a general purposed fuzzy shell for employment purposes. It will utilize fuzzy algorithms and optimization techniques to design an efficient and flexible system. Collaborating with Solware Information Technology Ltd. and Budapest University of Technology and Economics, the project goals include the development of a job searching subsystem and applicant searching subsystem, as well as the design and optimization of a fuzzy shell for employment. The system will provide a user-friendly interface on both web and Windows environments, with a SQL database and XML applications. The project will also include the implementation of learning algorithms for parameter optimization. Overall, this project aims to enhance the efficiency and accuracy of employment expert systems using fuzzy logic and intelligent information processing techniques.
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Intelligent Information Expert System for Employment and General Purposed Fuzzy Shell
Members of the Consortium • Technological centre: • Solware Information TechnologyLtd. • Tasks: • coordinator • project leader • software development • Knowledge centre: • Budapest University of Technology and Economics, Department of Telecommunication and TelematicsTasks: • fuzzy algorithms • aggregation • - general searching-offering system • - general fuzzy shell
Agenda • 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
Project Goals, Tasks Goal • Goal:Development of a specific and a general purposedfuzzy rule-based expert system • Two steps: • 1. Development of a fuzzy-based searching-offering system • 2. Development of a general purposed fuzzy-shell • Mile stones: • a. Fuzzy-based searching-offering subsystem • b. Job searching subsystem • c. Applicant searching subsystem • d. Fuzzy shell • e. Optimisation the parameters of employment (job and applicant searching) • f. Design fuzzy-shell demo program
Expert Systems Backgr. 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
Fuzzy Definitions Backgr. • Fuzzy set • A 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 logic • Generalisation of the two-valued Boole type logic 1 A=more then 3000 USD 0 income (USD) 3000 USD
Fuzzy Rule-BasedExpertSystems(FuzzyShell) Backgr. Rule-base If Then Inference engine Fuzzyfication unit Defuzzyfication unit Observation Crisp output Inference Algorithm i.e. Mamdani Sugeno, etc..
Backgr. Comparison • Advantage over the classical expert systems: • less rule • decreasecomputational complexity • c = decreasing factor against symbolical expertsystems • 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
Project Offer Search 1 0.45 0 Fuzzy Based Searching-Offering Systems Problems by finding the partners each other: 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 - in discrete case: search offer Similarity matrix
Project Employment Expert System • 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
Project Accomplishment User surfaces Job searching user surface Job offering user surface Employment agency surface Database Application logic Fuzzy system - user surface on web and windows environment - SQL database - XML, MTS applications Algorithms
Project Architecture of Employment Expert System SQL data base Internet client XML configuration files Internet client Application server Web server Internet client
Project Design, Optimisationof System Parameters Default parameters (employment experts) • optimisation on real data • the learning algorithm is a typeof evolutionary algorithm:bacterial algorithm Choose the parameters for optimisation Optimisation with learning methods Check the results on test set
Conclusions Summa • The method • - Advantages and disadvantages of fuzzy-based expertsystems • - Motivation of using fuzzy methods • Until now • General searching-offering system • right now: Job searching subsystem • Future • - Applicant searching subsystem • - General purposes fuzzy shell
