Agents modeling experience applied to control of semi-continuous production process

• Autorzy: Rojek G.

Identyfikatory

DOI

10.7494/csci.2014.15.4.411

• Języki publikacji: EN

Abstrakty

EN

The lack of proper analytical models of some production processes prevents us from obtaining proper values of process parameters by simply computing optimal values. Possible solutions of control problems in such areas of industrial processes can be found using certain methods from the domain of artificial intelligence: neural networks, fuzzy logic, expert systems, or evolutionary algorithms. Presented in this work, a solution to such a control problem is an alternative approach that combines control of the industrial process with learning based on production results. By formulating the main assumptions of the proposed methodology, decision processes of a human operator using his experience are taken into consideration. The researched model of using and gathering experience of human beings is designed with the contribution of agent technology. The presented solution of the control problem coincides with case-based reasoning (CBR) methodology.

Słowa kluczowe

EN

agent technology; industrial control; case-based reasoning

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2014
• Tom: Vol. 15 (4)
• Strony: 411--439
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Rojek G.

• AGH University of Science and Technology, Department of Computer Science, Krakow, Poland

Bibliografia

• [1] Aamodt A., Plaza E.: Case-based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, vol. 7(1), pp. 39–59,n1994.
• [2] Atanassov A., Antonov L.: Comparative analysis of case based reasoning software frameworks jCOLIBRI and myCBR. Journal of the University of Chemical Technology & Metallurgy, vol. 47(1), pp. 83–90, 2012.
• [3] Bellifemine F. L., Caire G., Greenwood D.: Developing Multi-Agent Systems with JADE. Wiley, 2007.
• [4] Bequette B.: Process Control: Modeling, Design, and Simulation. Prentice Hall Press, Upper Saddle River, NJ, USA, 2002.
• [5] Bergmann R., Althoff K. D., Minor M., Reichle M., Bach K.: Case-Based Reasoning – Introduction and Recent Developments. K ̈unstliche Intelligenz: Special Issue on Case-Based Reasoning, vol. 23(1), pp. 5–11, 2009.
• [6] Byrski A.: Tuning of agent-based computing. Computer Science, vol. 14(3), pp. 491–512, 2013.
• [7] Hornik K., Stinchcombe M., White H.: Multilayer Feedforward Networks Are Universal Approximators. Neural Networks, vol. 2(5), pp. 359–366, 1989.
• [8] Kisiel-Dorohinicki M.: Evolutionary Multi-Agent Systems in Non-Stationary Environments. Computer Scienc, vol. 14(4), pp. 563–575, 2013.
• [9] Leitao P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, vol. 22(7), pp. 979–991, 2009.
• [10] Richter M. M.: Introduction. In: M. Lenz, B. Bartsch-Sp ̈orl, H. D. Burkhard, S. Wess (eds.): Case-Based Reasoning Technology, From Foundations to Applications, pp. 1–16. Springer-Verlag, London, UK, 1998.
• [11] Shen W., Norrie D.H., Kremer R.: Developing Intelligent Manufacturing Systems Using Collaborative Agent Proceedings of the 2nd International Workshop on Intelligent Manufacturing Systems, pp. 157–166. 1999.
• [12] Sun J.: CBR Applications in Combustion Control of Blast Furnace Stoves. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2008, pp. 25–28. Newswood Limited, 2008.
• [13] Sztangret L., Rauch L., Kusiak J., Jarosz P., Ma lecki S.: Modeling of the oxidizing roasting process of zinc sulphide concentrates using the artificial neural networks. Computer Methods in Materials Science, vol. 1(11), pp. 122–127, 2011.
• [14] Watson I.: Case-based reasoning is a methodology not a technology. Knowledge- Based Systems, vol. 12(5-6), pp. 303–308, 1999.
• [15] Weiss G., ed.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge, MA, USA, 1999.
• [16] Woolridge M.: Introduction to Multiagent Systems. John Wiley & Sons, Inc., New York, NY, USA, 2001.