REVIEW OF METHODS FOR BUILDING AGENT SYSTEMS AND DECISION SUPPORT SYSTEMS

  • Marina Cherkasskaya Plekhanov Russian University of Economics
  • Alexey Artamonov Plekhanov Russian University of Economics
  • Andrey Cherkasskiy National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Keywords: multi-agent systems, decision support systems, distributed artificial intelligence, Data Mining, decision trees, neural network, genetic algorithm, expert systems

Abstract

This article presents data on the properties and features of agents’ behavior, ways of their communication and multi-agent systems based on them. Criteria for building decision support systems using modern tools, basic stages of designing and maintaining information repositories, technologies of operational and mining data analysis, as well as genetic algorithms and knowledge models in expert systems are considered. A set of tools is described that provide adequate reality forecasts that help to obtain the necessary information for making decisions in the conditions of market competition.

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References

Galperov, V. I. 2007. Methods, models and algorithms for constructing multi-agent systems in the energy sector: on the example of the task of assessing the state of electric power systems (PhD thesis). Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia.

Yesikova, T. N. and Zaytsev, I. D. 2010. Development of the agent model “Evaluation of the strategic directions of the backbone transport network of Russia with different geoeconomic architectonics of the WORLD Economic System”. Large-scale Systems Management (MLSD'2010): Proceedings of the Fourth International Conference. Moscow, Russia, pp. 107-114.

Russell, S. and Norvig, P. (2009). Artificial intelligence: a modern approach (3-d edition). Pearson Education Limited.

Garro, A., Mühlhäuser, M., Baldoni, M., Bergenti, F., and Torroni P. 2018. Intelligent Agents: Multi-Agent Systems. Encyclopedia of Bioinformatics and Computational Biology, 1: 315-320.

Zaytsev, I. D. 2014. Multi-agent systems in the modeling of socio-economic relations: the study of behavior and verification of properties using Markov chains (PhD thesis). A.P. Ershov Institute of Informatics Systems (IIS), Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.

Chaib-Draa, B., Moulin, M., Mandiau, R., and Millot, P. 1992. Trends in Distributed Artificial Intelligence. Artificial Intelligence Review, 6: 35-66.

Tarasov, V. B. 1998. Agents, multi-agent systems, virtual communities: a strategic direction in computer science and artificial intelligence. Artificial Intelligence News, 2: 5-63.

Bolotova, L. S. 2012. Systems and methods of artificial intelligence: knowledge-based models and technologies. Moscow: Finance and Statistics,.

Cohen, P. R., and Levesque, H. J. 1990. Intention is Choice with Commitment. Artificial Intelligence, 42: 213-262.

Mitrakov, A. A. 2013. Approaches to agent-based systems designing. Materials of the V International Student Scientific Conference "Student Scientific Forum". Moscow, Russia, pp. 1-15.

Smith, R. G. (1980). The Сontract Net Protocol: High Level Communication and Control in a Distrubuted Problem Solver. IEEE Transactions on Computers, 29(12): 1104-1113.

Rosenshein, J. and Zlotkin, G. 1994. Rules of Encounter: Designing Conventions for Automated Negociation Among Computers. Cambridge MA: MIT Press.

Gorodetsky, V. I., Grushinsky M.S., and Khabalov A.V. 2008. Multi-Agent Systems (Overview). Artificial Intelligence News, 2: 64-105.

Paklin, N. B. and Oreshkov, V.I. 2010. Business Intelligence: From Data to Knowledge. Saint Petersburg: Piter.

Prokopenko, N. U. 2017. Decision Support Systems Based on Deductor Studio Academic 5.3. NNSAGU, Nizhny Novgorod.

Zaskanov, V. G., Ivanov, D.U., and Grishanov, G. M. 2013. Decision Support Systems. Samara National Research University, Samara.

Barsegyan, A. A. 2007. Data Analysis Technologies: Data Mining, Visual Mining, Text Mining, OLAP. Saint Petersburg: BHV-Petersburg.

Cao, L., Gorodetsky, V. I., and Mitkas, P. A. (2009). Agents and Data Mining. IEEE Intelligent Systems, 24(3): 64-72.

Kulik, S. 2015. Neural network model of artificial intelligence for handwriting recognition. Journal of Theoretical and Applied Information Technology, 73(2): 202-211.

Kulik, S., and Protopopova, U. 2020. Educational Intelligent System Using Genetic Algorithm. Procedia Computer Science, 169: 168-172.

Published
2020-11-13
How to Cite
Cherkasskaya, M., Artamonov, A., & Cherkasskiy, A. (2020). REVIEW OF METHODS FOR BUILDING AGENT SYSTEMS AND DECISION SUPPORT SYSTEMS. COMPUSOFT: An International Journal of Advanced Computer Technology, 9(10), 3892-3899. Retrieved from https://ijact.in/index.php/ijact/article/view/1235