Resource model for heterogenous distributed computer system with local connections and its graph.
Keywords:
Parallel and distributed computer systems, resource model for computer system, artificial intelligence tasks, game trees, Monte-Carlo tree search method, MCTS, MCTS parallelization.
Abstract
Basing on the MCTS method analysis and approaches to its parallelization the paper proposes a new resource model of a heterogeneous distributed computer system with local connections (HDCSLC) and its graph. Differences of the model and its graph from the known generalized graph of a resource system are shown. It is supposed that the proposed model and graph would allow to create new resource planning techniques for HDCSLC with the aim of implementation of various kinds of MCTS parallelization.
References
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O.I.Marchenko. Structure and criteria for classification of techniques for implementation and improvement of Monte-Carlo tree search./ O.I.Marchenko, O.O.Marchenko // Komp’uterno-integrovani tekhnologii: osvita, nauka, vyrobnytstvo, No.21, 2015, pp.51-57 (in Ukrainian).
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O.O.Marchenko. “Depth-Width” Criterion for Control of the Search Tree Shape Using Monte-Carlo Method./ O.O.Marchenko, O.I.Marchenko // Komp’uterno-integrovani tekhnologii: osvita, nauka, vyrobnytstvo, No.24-25, 2016, pp.42-47 (in Ukrainian).
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O.O.Marchenko. Technique for MCTS dynamic parallelization for grid-systems./ O.O.Marchenko, O.I.Marchenko // Vymiriuval’na ta obchysliuval’na tekhnika v tekhnolohichnykh procesakh, No.3(59), 2017, pp.37-43 (in Ukrainian).
Hilmar Finnsson. Game-Tree Properties and MCTS Performance. / Hilmar Finnsson and Yngvi Björnsson // GIGA 2011: Proceedings of the 2nd International General Game Playing Workshop, 2011, pp.23-30.
G. M. J.-B. Chaslot. Parallel Monte-Carlo Tree Search / G. M. J.-B. Chaslot, M. H. M. Winands, and H.J. van den Herik // Proc. Comput. And Games, LNCS 5131, Beijing, China. – 2008. P.60–71.
T. Cazenave. On the Parallelization of UCT / T. Cazenave and N. Jouandeau // Proc. Comput. Games Workshop, Amsterdam, Netherlands. – 2007. – P. 93–101.
Y. Soejima. Evaluating Root Parallelization in Go / Y. Soejima, A. Kishimoto, and O. Watanabe // IEEE Trans. Comp. Intell. AI Games. – vol. 2. – no. 4. – 2010. – P. 278–287.
A. Bourki. Scalability and Parallelization of Monte-Carlo Tree Search / A. Bourki, G. M. J.-B. Chaslot, M. Coulm, V. Danjean, H. Doghmen, J.-B. Hoock, T. Heґrault, A. Rimmel, F. Teytaud, O. Teytaud, P. Vayssie`re, and Z. Yu // Proc. Int. Conf. Comput. and Games, LNCS 6515, Kanazawa, Japan. – 2010. – P. 48–58.
Simonenko V.P. Organization of computing processes in computers, complexes, networks, and systems. // Kiev, TOO “VEK+”. – 1997. – 302 p.
Schaeer, J. The APHID Parallel algorithm / Schaeer, J., Brockington M. G. // Proceedings of the 8th IEEE Symposium on Parallel and Distributed Processing. – 1996. – P. 428-432.
O.I.Marchenko. Structure and criteria for classification of techniques for implementation and improvement of Monte-Carlo tree search./ O.I.Marchenko, O.O.Marchenko // Komp’uterno-integrovani tekhnologii: osvita, nauka, vyrobnytstvo, No.21, 2015, pp.51-57 (in Ukrainian).
O.I.Marchenko. Classification of Monte-Carlo Tree Search Enhancement Techniques Oriented to Specifics of the Method./ O.I.Marchenko, O.O.Marchenko, M.M.Orlova // Artificial Intelligence, No.2(72), 2016, pp.59-69 (in Ukrainian).
O.O.Marchenko. “Depth-Width” Criterion for Control of the Search Tree Shape Using Monte-Carlo Method./ O.O.Marchenko, O.I.Marchenko // Komp’uterno-integrovani tekhnologii: osvita, nauka, vyrobnytstvo, No.24-25, 2016, pp.42-47 (in Ukrainian).
Oleksandr I. Marchenko. Monte-Carlo Tree Search with Tree Shape Control. / Oleksandr I. Marchenko, Oleksii O. Marchenko // 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). Conference Proceedings. May 29 – June 2, 2017., Kyiv, Ukraine. – 2017. – P. 812-8173.
O.O.Marchenko. Logarithmic criterion for tree shape control for improvement of the Monte-Carlo tree search method./ O.O.Marchenko, O.I.Marchenko // Komp’uterno-integrovani tekhnologii: osvita, nauka, vyrobnytstvo, No.27, 2017, pp.37-43 (in Ukrainian).
O.O.Marchenko. Technique for MCTS dynamic parallelization for grid-systems./ O.O.Marchenko, O.I.Marchenko // Vymiriuval’na ta obchysliuval’na tekhnika v tekhnolohichnykh procesakh, No.3(59), 2017, pp.37-43 (in Ukrainian).
Hilmar Finnsson. Game-Tree Properties and MCTS Performance. / Hilmar Finnsson and Yngvi Björnsson // GIGA 2011: Proceedings of the 2nd International General Game Playing Workshop, 2011, pp.23-30.
G. M. J.-B. Chaslot. Parallel Monte-Carlo Tree Search / G. M. J.-B. Chaslot, M. H. M. Winands, and H.J. van den Herik // Proc. Comput. And Games, LNCS 5131, Beijing, China. – 2008. P.60–71.
T. Cazenave. On the Parallelization of UCT / T. Cazenave and N. Jouandeau // Proc. Comput. Games Workshop, Amsterdam, Netherlands. – 2007. – P. 93–101.
Y. Soejima. Evaluating Root Parallelization in Go / Y. Soejima, A. Kishimoto, and O. Watanabe // IEEE Trans. Comp. Intell. AI Games. – vol. 2. – no. 4. – 2010. – P. 278–287.
A. Bourki. Scalability and Parallelization of Monte-Carlo Tree Search / A. Bourki, G. M. J.-B. Chaslot, M. Coulm, V. Danjean, H. Doghmen, J.-B. Hoock, T. Heґrault, A. Rimmel, F. Teytaud, O. Teytaud, P. Vayssie`re, and Z. Yu // Proc. Int. Conf. Comput. and Games, LNCS 6515, Kanazawa, Japan. – 2010. – P. 48–58.
Simonenko V.P. Organization of computing processes in computers, complexes, networks, and systems. // Kiev, TOO “VEK+”. – 1997. – 302 p.
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Published
2020-05-18
How to Cite
MarchenkoО., & MarchenkoО. (2020). Resource model for heterogenous distributed computer system with local connections and its graph. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (39), 83-88. https://doi.org/10.36910/6775-2524-0560-2020-39-14
Section
Automation and Control
