GAME MODEL OF ONTOLOGICAL PROJECT SUPPORT
Keywords:multi-agent system, ontology, project, stochastic game, adaptive game method.
Context. In today’s information society with advanced telecommunications through mobile devices and computer networks, it is important to form a variety of virtual organizations and communities. Such virtual associations of people by professional or other interests are designed to quickly solve various tasks: to perform project tasks, create startups to attract investors, network marketing, distance learning, solving complex problems in science, economics and public administration , construction of various Internet services, discussion of political and social processes, etc.
Objective of the study is to develop an adaptive Markov recurrent method based on the stochastic approximation of the modified condition of complementary non-rigidity, valid at Nash equilibrium points for solving the problem of game coverage of projects.
Method. In this work the multiagent game model for formation of virtual teams of executors of projects on the basis of libraries of subject ontologies is developed. The competencies and abilities of agents required to carry out projects are specified by sets of ontologies. Intelligent agents randomly, simultaneously and independently choose one of the projects at discrete times. Agents who have chosen the same project determine the current composition of the team of its executors. For agents’ teams, a current penalty is calculated for insufficient coverage of competencies by the combined capabilities of agents. This penalty is used to adaptively recalculate mixed player strategies. The probabilities of selecting those teams whose current composition has led to a reduction in the fine for non-coverage of ontologies are increasing. During the repetitive stochastic game, agents will form vectors of mixed strategies that will minimize average penalties for non-coverage of projects.
Results. For solve the problem of game coverage of projects, an adaptive Markov recurrent method based on the stochastic approximation of the modified condition of complementary non-rigidity, valid at Nash equilibrium points, was developed.
Conclusions. Computer simulation confirmed the possibility of using the stochastic game model to form teams of project executors with the necessary ontological support in conditions of uncertainty. The convergence of the game method is ensured by compliance with the fundamental conditions and limitations of stochastic optimization. The reliability of experimental studies is confirmed by the repeatability of the results obtained for different sequences of random variables.
Virtual Communities: Concepts, Methodologies, Tools and Applications, Information Resources Management Association (USA), Vol. 1–4. Hershey, IGI Global, 2011, 2930 p. DOI: 10.4018/978-1-60960-100-3.
Hutchings T. Real Virtual Community, Word & World, 2015, Vol. 35, No. 2, pp. 151–161.
Roy A. A Typology of Virtual Communities on the Internet: Contingency Marketing Approaches, Marketing & Tourism : First International Academic Research Conferenceon, Dubai-UAE, 22–24 May 2015 : proceedings. Dubai, MTCI, 2015, pp. 1–11.
Weiss G. Multiagent Systems. Cambridge, The MIT Press, 2016, 920 p.
Byrski A., Kisiel-Dorohinicki M. Evolutionary Multi-Agent Systems: From Inspirations to Applications. Cham, Springer International Publishing, 2017, 210 p. DOI: 10.1007/978-3319-51388-1
Radley N. Multi-Agent Systems – Modeling, Control, Programming, Simulations and Applications. Wilmington, Scitus Academics Llc, 2017. – 276 p.
Dignum F., Bradshaw J., Silverman B. G., Doesburg W. Agent for Games and Simulations: Trends in Techniques, Concepts and Design. Cham, Springer International Publishing, 2009, 237 p. DOI : 10.1007/978-3-642-11198-3
Kravets P., Burov Y., Lytvyn V., Vysotska V. Gaming Method of Ontology Clusterization, Webology, 2019, Vol. 16, № 1, pp. 55–76.
Stuart D. Practical Ontologies for Information Professionals. London, Facet Publishing, 2016, 224 p.
Aleman Y., Somodevilla M. J. A proposal for domain ontological learning, Research in Computing Science, 2017, Vol. 133, pp. 63–70.
Keet C. M. An introduction to Ontology Engineering [Electronic resource]. Access mode: http://hdl.handle.net/11427/28312.
Thomas C. Ontology in Information Science. London, IntechOpen, 2018, 132 p. DOI: 10.5772/65599.
Sun Z. Cooperative Coordination and Formation Control for Multi-agent Systems. Cham, Springer International Publishing, 2018, 179 p. DOI : 10.1007/978-3-319-74265-6
Yang S., Xu J.-X., Li X., Shen D. Iterative Learning Control for Multi-agent Systems Coordination. New York, WileyIEEE Press, 2017, 272 p.
Scerri P., Vincent R., Mailler R. T. Coordination of LargeScale Multiagent Systems. Cham, Springer International Publishing, 2010, 352 p. DOI : 10.1007/0-387-27972-5
Perminova-Harikovski O., Gustafsson M., Wikstrom K. Defining Uncertainty in Projects – A New Perspective, International Journal of Project Management, 2008, Vol. 26, No. 1, pp. 73–79. DOI: 10.1016/j.ijproman.2007.08.005
Zheng E.Z.H. Managing Uncertainty in Projects: A Review, Trends and Gaps. Revista de Gestao e Projetos GeP / E.Z.H. Zheng, M.M. Carvalho // Journal of Business and Projects. – 2016. – Vol. 7, № 2. – P. 95-99.
Cleden D. Managing Project Uncertainty (Advances in Project Management). New York, Routledge, 2017. – 138 p.
Macedo K., M. Marinho, S. Santos Uncertainty Management in Software Projects: A Case Study in a Public Company, Journal of Convergence Information Technology, 2019, Vol. 14, No. 1, pp. 61-67.
Bryl V., Giorgiani P., Fante S. ToothAgent: A Multi-agent System for Virtual Communities Support, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, 2008, Vol. 4898, pp. 212–230. DOI: 10.1007/978-3-540-779902_13
Fahad M., Boissier O., Maret P., Moalla N., Gravier C. Smart Places: Multi-Agent based Smart Mobile Virtual Community Management System, Applied Intelligence. Springer Verlag, Germany, 2014, Vol. 41, No. 4, pp. 10241042. – DOI: 10.1007/s10489-014-0569-2.hal-01015456
Lee Y., Chong Q. Multi-agent Systems Support for Community-Based Learning, Interacting with Computers, 2003, Vol. 15, No. 1, pp. 33–55. DOI: 10.1016/S09535438(02)00057-7
Hidalgo-Herrero M., Rabanal P., Rodriguez I., Rubio F. Comparing problem solving strategies for NP-hard optimization problems, Fundamenta Informaticae, 2013, Vol. 124, No. 1–2, pp. 1–25.
Abdulrahman S. M. Using Swarm Intelligence for Solving NP-hard, Academic Journal of Nawroz University, 2017, Vol. 6, No. 3, pp. 46-50.
Huang X. A polynomial-time algorithm for solving NP-hard problems in practice, ACM SIGACT News, 2003, Vol. 34, No. 1, pp. 101–108.
Reus B. How to Solve NP-Complete Problems. Limits of Computation. Undergraduate Topics in Computer Science. Springer, Cham, 2016, pp. 275–297. DOI: 10.1007/978-3319-27889-6_21.
Panchal G. Panchal D. Solving NP hard Problems using Genetic Algorithm, International Journal of Computer Science and Information Technology, 2015, Vol. 6, No. 2, pp. 1824–1827.
Prates M., Avelar P. H. C., Lemos H., Lamb L. C., Vardi M. Y. Learning to solve NP-complete problems : A graph neural network for decision TSP, Artificial Intelligence : The Thirty-Third AAAI Conference, Honolulu, Hawaii, USA, January 27 – February 1, 2019 : proceedings. Honolulu, Hilton Hawaiian Village, 2019, Vol. 33, No. 1, pp. 4731-4738. DOI : 10.1609/aaai.v33i01.33014731
Chen B.-S. Stochastic Game Strategies and their Applications. Boca Raton, CRC Press, 2019, 610 p.
Ummels M. Stochastic Multiplayer Games: Theory and Algorithms. Amsterdam, Amsterdam University Press, 2014, 174 p.
Ungureanu V. Pareto-Nash-Stackelberg Game and Control Theory: Intelligent Paradigms and Applications. Cham, Springer International Publishing, 2018, 343 p. DOI: 10.1007/978-3-319-75151-1
Neogy S. K., Bapat R. B., Dubey D. Mathematical Programming and Game Theory. Cham, Springer International Publishing, 2018, 226 p. DOI : 10.1007/978981-13-3059-9
Nazin A. V., Poznyak A. S. Adaptivnyi vybor variantov: Rekurrentnye algoritmy. Moscow, Nauka, 1986, 288 p.
Kushner H., Yin G. Stochastic Approximation and Recursive Algorithms and Applications. New York, Springer-Verlag, 1997, 417 p. DOI : 10.1007/978-1-48992696-8
Benveniste A., Metivier M., Priouret P. Adaptive Algorithms and Stochastic Approximations. Berlin, Springer-Verlag Berlin Heidelberg, 1990, 365 p. DOI : 10.1007/978-3-642-75894-2
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