Steel Bridge Fabrication Errors Indexed Examples and Solutions: Combining Rules and Cases

This research focuses on the development of a knowledge-based system in the domain of steel bridge fabrication errors using both rule-based reasoning (RBR) and case-based reasoning (CBR). Fabrication error Indexed and Solutions (FIXS) was developed to combine the benefits of two previous research projects: 1) the rule-based Bridge Fabrication error solution eXpert system (BFX), and 2) its case-based counterpart (CB-BFX). Errors that occur during the fabrication of steel bridg~ members can have a costly effect on the performance of a bridge if not repaired properly. FIXS is an effort to provide guidance to the bridge engineer responsible for cost effective solutions in a time sensitive manner. FIXS is implemented in the programming language PROLOG and runs in the Windows environment as a stand-alone application. RBR facilities are provided by the expert system shell MESS (Modest Expert System Shell). Similarly, CBR functions are provided by the simple case-based reasoner shell SCBR (Simple Case Based Reasoning). The application has been designed for addition of new domain knowledge. The addition of new and updated knowledge allows the application to keep pace with changes in the steel bridge design industry and the methods of repairing errors

Expert System for Structural Optimization Exploiting Past Experience and A-priori Knowledge.

The availability of comprehensive Structural Optimization Systems in the market is allowing designers direct access to software tools previously the domain of the specialist. The use of Structural Optimization is particularly troublesome requiring knowledge of finite element analysis, numerical optimization algorithms, and the overall design environment. The subject of the research is the application of Expert System methodologies to support nonspecialists when using a Structural Optimization System. The specific target is to produce an Expert System as an adviser for a working structural optimization system. Three types of knowledge are required to use optimization systems effectively; that relating to setting up the structural optimization problem which is based on logical deduction; past, experience; together with run-time and results interpretation knowledge. A knowledge base which is based on the above is set, up and reasoning mechanisms incorporating case based and rule based reasoning, theory of certainty, and an object oriented approach are developed. The Expert SVstem described here concentrates on the optimization formulation aspects. It is able to set up an optimization run for the user and monitor the run-time performance. In this second mode the system is able to decide if an optimization run is likely to converge to a, solution and advice the user accordingly. The ideas and Expert System techniques presented in this thesis have been implemented in the development; of a prototype system written in C++. The prototype has been extended through the development of a user interface which is based on XView

Perancangan dan Implementasi Sistem Pakar Mendeteksi Tingkat Depresi Seseorang

ABSTRAKSI: Depresi merupakan salah satu penyakit dari gangguan jiwa. Sebagian besar masyarakat tidak menyadari bahwa penyakit ini dapat diderita oleh semua orang. Adapun jenis tingkatan yang ada terhadap depresi ini yaitu antara lain bebas depresi, ringan, sedang dan berat. Permasalahan yang timbul adalah semakin kurang dan tidak meratanya penyebaran dokter spesialis jiwa atau pakar yang dapat mengatasi masalah penyakit depresi. Sehingga banyak masyarakat mengalami kesulitan bila ingin mendeteksi apakah mereka menderita penyakit depresi atau tidak. Sistem pakar mendeteksi tingkat depresi seorang ini dapat mengetahui tingkat depresi yang diderita seseorang. Pengembangan sistem pakar pendeteksi depresi ini mengimplementasikan metode Case-based Reasoning sebagai basis pengetahuannya sedangkan mesin inferensi menggunakan metode Backward Chaining. Kata Kunci : Sistem pakar,Case Based Reasoning , Backward chaining, depresi.ABSTRACT: Depression is a kind of mental disorder. Most of people don’t realize that this disorder can be had by everyone. The kinds of the level of this depression are three of depression low, medium and high level. The problem that appears is less and not balance in spreading of mental specialist or the expert who can solve the problem of this depression. This make a lot of people have a difficulty to detect if they have depression or not. This expert system to detect depression level can detect the depression level of someone. The development of this expert system is implemented by Case-based reasoning method as its knowledge base and Backward Chaining method as its inference machine.Keyword: Expert system, Case-based Reasoning, Backward Chaining, Depressio

A large case-based reasoner for legal cases

Case-Based Reasoning Research and Development: Proceedings of the 2nd International Conference on Case-Based Reasoning, ICCBR 1997: pp. 190-199.In this paper we propose a large case-based reasoner for the legal domain. Analyzing legal texts for indexing purposes makes the implementation of large case bases a complex task. We present a methodology to automatically convert legal texts into legal cases guided by domain expert knowledge in a rule-based system with Natural Language Processing (NLP) techniques. This methodology can be generalized to be applied in different domains making Case-Based Reasoning (CBR) paradigm a powerful technology to solve real world problems with large knowledge sources

Decision Support System Selection Position Of High Leadership Of Pratama In Katingan Regency With Simple Additive Weighting Method

The issue addressed in this research is how to design and build an expert systemwhich capable of diagnosing dog diseases that are caused by parasites, and how this expertsystem later can be useful for dog owners.This expert system is developed based on website technology in order to ease theaccessibility for the users. The finished product is expected to become useful for dog owners,to help them diagnose their dog disease based on the symptoms in order to give the propertreatment. The software development methodology used to develop this expert system is theWaterfall methodology, while the design is done using Unified Modeling Language (UML).The programming language used to build the expert system is PHP for the logic and MySQLfor the database, and the finished product is then tested using black box testing method. Totest system accuracy, disease diagnosis results from the expert system is then compared withdiagnosis results from the real human expert (veterinarian).This expert system is using forward chaining method and Bayes theorem. The forwardchaining concept, in this system, starting with the symptoms that are set into a reasoning andrules to reach some conclusions which in this case are the possible diseases. While the Bayestheorem is used to draw a conclusion from the possible diseases by calculating the probabilityof what kind of disease suffered based on the symptoms, and the weight of the probabilityitself. This expert system, however, still can be developed not only to diagnose diseases thatare caused by parasites, but also to diagnose all kind of dog diseases

SHYSTER and the authorization of copyright infringement

The SHYSTER project is concerned with the development of a hybrid legal expert system: one which uses rule-based techniques to represent statute law and case-based techniques to represent case law. A prototype has been developed, implementing the case-based part of SHYSTER. This prototype allows a legal expert to specify an area of case law using a specially-developed case law specification language. The model of legal reasoning which has been adopted for the development of SHYSTER is explained, and the operation of the prototype is illustrated by example. An area of case law (the meaning of “authorization" in the Copyright Act ) is specified, and SHYSTER is made to generate opinions about real cases on the basis of that specification. SHYSTER's legal opinions are compared with the actual judgments in those cases. SHYSTER's case-based system performs well in two quite different legal domains, suggesting that SHYSTER's approach to case law may prove successful in other areas of law. Avenues of future research are identified, and the manner in which SHYSTER's case-based system will be linked with a rule-based system to form a hybrid legal expert system is explained

Web-based CBR System for Support Medical Diagnosis

From the early days of development of Artificial Intelligence, there was a strong in-terest in applications in the area of medicine. The interest was strong enough to form a separate branch in the early ‘80s entitled Artificial Intelligence in Medicine (AIM). DXplain [1] is an illustration of system from this early period. It is an internal medi-cine expert system developed at the Massachusetts General Hospital that is still in use at a number of hospitals and medical schools, mostly for clinical education purposes. Rather than using an expert system approach or another rule-inferring paradigm we decided to employ a Case-based Reasoning (CBR) methodology [4]. Storing and searching among past cases has an advantage in complex domains where it is difficult to create a global theory that explains most of the existing cases

CBR model for the intelligent management of customer support centers

[EN] In this paper, a new CBR system for Technology Management Centers is presented. The system helps the staff of the centers to solve customer problems by finding solutions successfully applied to similar problems experienced in the past. This improves the satisfaction of customers and ensures a good reputation for the company who manages the center and thus, it may increase its profits. The CBR system is portable, flexible and multi-domain. It is implemented as a module of a help-desk application to make the CBR system as independent as possible of any change in the help-desk. Each phase of the reasoning cycle is implemented as a series of configurable plugins, making the CBR module easy to update and maintain. This system has been introduced and tested in a real Technology Management center ran by the Spanish company TISSAT S.A.Financial support from Spanish government under grant PROFIT FIT-340001-2004-11 is gratefully acknowledgeHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Julian Inglada, VJ.; Rebollo Pedruelo, M.; Botti, V. (2006). CBR model for the intelligent management of customer support centers. En Lecture Notes in Computer Science. Springer Verlag (Germany). 663-670. https://doi.org/10.1007/11875581_80S663670Acorn, T., Walden, S.: SMART: SupportManagement Automated Reasoning Technology for Compaq Customer Service. In: Scott, A., Klahr, P. (eds.) Proceedings of the 2 International Conference on Intelligent Tutoring Systems, ITS-92 Berlin, vol. 4, pp. 3–18. AAAI Press, Menlo Park (1992)Simoudis, E.: Using Case-Based Retrieval for Customer Technical Support. IEEE Intelligent Systems 7, 10–12 (1992)Kriegsman, M., Barletta, R.: Building a Case-Based Help Desk Application. IEEE Expert: Intelligent Systems and Their Applications 8, 18–26 (1993)Shimazu, H., Shibata, A., Nihei, K.: Case-Based Retrieval Interface Adapted to Customer-Initiated Dialogues in Help Desk Operations. In: Mylopoulos, J., Reiter, R. (eds.) Proceedings of the 12th National Conference on Artificial Intelligence, vol. 1, pp. 513–518. AAAI Press, Menlo Park (1994)Raman, R., Chang, K.H., Carlisle, W.H., Cross, J.H.: A self-improving helpdesk service system using case-based reasoning techniques. Computers in Industry 2, 113–125 (1996)Kang, B.H., Yoshida, K., Motoda, H., Compton, P.: Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11, 611–631 (1997)Roth-Berghofer, T., Iglezakis, I.: Developing an Integrated Multilevel Help-Desk Support System. In: Proceedings of the 8th German Workshop on Case-Based Reasoning, pp. 145–155 (2000)Goker, M., Roth-Berghofer, T.: The development and utilization of the case-based help-desk support system HOMER. Engineering Applications of Artificial Intelligence 12, 665–680 (1999)Roth-Berghofer, T.R.: Learning from HOMER, a case-based help-desk support system. In: Melnik, G., Holz, H. (eds.) Advances in Learning Software Organizations, pp. 88–97. Springer, Heidelberg (2004)Bergmann, R., Althoff, K.D., Breen, S., Göker, M., Manago, M., Traphöner, R., Wess, S.: Developing Industrial Case-Based Reasoning Applications. In: The INRECA Methodology, 2nd edn. LNCS (LNAI), vol. 1612. Springer, Heidelberg (2003)eGain (2006), http://www.egain.comKaidara Software Corporation (2006), http://www.kaidara.com/Empolis Knowledge Management GmbH - Arvato AG (2006), http://www.empolis.com/Althoff, K.D., Auriol, E., Barletta, R., Manago, M.: A Review of Industrial Case-Based Reasoning Tools. AI Perspectives Report. Goodall, A., Oxford (1995)Watson, I.: Applying Case-Based Reasoning. Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc. California (1997)empolis: empolis Orenge Technology Whitepaper. Technical report, empolis GmbH (2002)Tissat, S.A. (2006), http://www.tissat.esGiraud-Carrier, C., Martinez, T.R.: An integrated framework for learning and reasoning. Journal of Artificial Intelligence Research 3, 147–185 (1995)Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yanez, J.C.: Neuro-symbolic system for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 1–10. Springer, Heidelberg (2004)Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1997

Knowledge Based Management For Rotating Equipment Diagnostics

Knowledge based management system for rotating equipment diagnostics is a expert system to help the maintenance engineers in the power plants or gas plants using gas turbines for power generation. Industrial gas turbine (Rolls - Royce Allison 510 - KB7) is used as the emphasis of the project to develop the application. Case based Reasoning and Spiral Life Cycle model are used as the methodology in this project for the methods can support and fulfil the objectives of the project. Microsoft Access and Java runtime are used for the database set up and system development respectively. The final system offers eight different scenarios for gas turbine diagnostics. Reference tables and Scenario note function The system is effective and less time consuming, platform( operating system) independent, easy to use and should be helpful for the maintenance engineers. Diagnostics for the auxiliary system of the gas turbine should be incorporated in the system to have a more complete system. MySQL database system should be used in the future development if the database is to expand