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Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets

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Języki publikacji
EN
Abstrakty
EN
The decision-theoretic rough set (DTRS) model considers costs associated with actions of classifying an equivalence class into a particular region. With DTRS, one may make informative decisions in the form of three-way decisions. Current research mainly focuses on single agent DTRS which is too complex for making a decision when multiple agents are involved. We propose a multiagent DTRS model and express it in the form of three-way decisions. The new model seeks for synthesized or consensus decisions when there aremultiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the conservative, aggressive and majority viewpoints based on the positive, negative and boundary regions made by each agent. These multi-agent decision regions are expressed by figures in the form of three-way decisions.
Wydawca
Rocznik
Strony
157--171
Opis fizyczny
Bibliogr. 26 poz., tab.
Twórcy
autor
autor
  • School of Mathematics, Physics & Information Science, Zhejiang Ocean University 18 Haiyuan Road, Dinghai, Zhoushan, Zhejiang, P. R. China, 316004, yxpzyp@sina.com
Bibliografia
  • [1] Chau, M., Zeng, D., Chen, H., Huang, M., Hendriawan, D.: Design and evaluation of a multi-agent collaborativeWeb mining system, Decision Support Systems, 35(1), 2003, 167-183.
  • [2] Herbert, J. P., Yao, J. T.: Game-theoretic risk analysis in decision-theoretic rough sets, Proc. Rough Sets and Knowledge Technology (G.Wang, T. Li, J. W. Grzymala-Busse, D. Miao, A. Skowron, Y. Yao, Eds.), LNCS (LNAI), 5009, Springer-Heidelberg, 2008, 132-139.
  • [3] Herbert, J. P., Yao, J. T.: Criteria for choosing a rough set model, Journal of Computing and Informatics, 57 (6), 2009, 908-918.
  • [4] Herbert, J. P., Yao, J. T.: Game-theoretic rough sets, Fundamenta Informaticae, 108(3-4), 2011, 267-286.
  • [5] Keeney, R. L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Trade-Offs, Cambridge University Press, 1993
  • [6] Lingras, P., Chen,M.,Miao, D. Q.: Rough cluster quality index based on decision theory, IEEE Transactions on Knowledge and Data Engineering, 21(7), 2009, 1014-1026.
  • [7] Liu, D., Li, H. X., Zhou, X. Z.: Two decades' research on decision-theoretic rough sets, Proc. 9th IEEE Int. Conf. on Cognitive Informatics (F. Sun, Y. Wang, J. Lu, B. Zhang, W. Kinsner, L.A. Zadeh, Eds.), 2010, 968-973.
  • [8] Liu, D., Yao, Y. Y., Li, T. R.: Three-way decision-theoretic rough sets, Computer Science, 38(1), 2011, (In Chinese), 246-250.
  • [9] Liu, Y., Bai, G., Feng, B.: Multi-agent based multi-knowledge acquisition method for rough set, Proc. Rough Sets and Knowledge Technology (G.Wang, T. Li, J.W. Grzymala-Busse, D.Miao, A. Skowron, Y. Yao, Eds.), LNCS (LNAI), 5009, Springer-Heidelberg, 2008, 140-147.
  • [10] O'Hare, G. M. P., O'Grady, M. J.: Gulliver's Genie: A multi-agent system for ubiquitous and intelligent content delivery, Computer Communications, 26(11), 2003, 1177-1187.
  • [11] Pawlak, Z.: Rough sets, International Journal of Computer and Information Sciences, 26(11), 1982, 341-356.
  • [12] Slezak, D, Ziarko,W.: The investigation of the Bayesian rough set model. International Journal Approximate Reasoning, 40, 2005, 81-89.
  • [13] Slezak, D.: Rough sets and Bayes factor, LNCS Transactions on Rough Sets III(J. Peters, A. Skowron, Eds.), LNCS 3400, Springer-Heidelberg, 2005, 202-229.
  • [14] Xu, Z. S.: Intuitionistic preference relations and their application in group decision making, Information Sciences, 177, 2007, 2363-2379.
  • [15] Xu, Z. S., Yager, R. R.: Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems, 35, 2006, 417-433.
  • [16] Yang, X. P., Yao, J. T.: A multi-agent decision-theoretic rough set model, Proc. Rough Sets and Knowledge Technology (J. Yu, S. Greco, P. Lingras, G. Wang, A. Skowron, Eds.), LNCS (LNAI), 6401, Springer-Heidelberg, 2010, 711-718.
  • [17] Yao, J. T., Herbert, J. P.: Financial time-series analysis with rough sets, Applied Soft Computing, 3, 2009, 1000-1007.
  • [18] Yao, J. T., Herbert, J. P.: Web-based support systems with rough set analysis, Rough Sets and Intelligent Systems Paradigms (M. Kryszkiewicz, J. F. Peters, H. Rybinski, A. Skowron, Eds.), LNCS (LNAI) 4585, Springer-Heidelberg, 2007, 360-370.
  • [19] Yao, Y. Y.: Probabilistic approaches to rough sets, Expert Systems, 20, 2003, 287-297.
  • [20] Yao, Y. Y., Wong, S. K. M.: A decision theoretic framework for approximating concepts, International Journal of Man-machine Studies, 37, 1992, 793-809.
  • [21] Yao, Y. Y., Wong, S. K. M., Lingras, P.: A decision-theoretic rough set models, Proc. 5th Int. Symposium on Methodologies for Intelligent Systems ( Z. W. Ras, M. Zemankova,M. L. Emrich, Eds.), 5, 1990, 17-24.
  • [22] Yao, Y. Y.: Three-way decisions with probabilistic rough sets, Information Sciences, 180, 2010, 341-353.
  • [23] Yao, Y. Y.: The superiority of three-way decisions in probabilistic rough sets models, Information Sciences, 181, 2011, 1080-1096.
  • [24] Zhao, W. Q., Zhu, Y. L.: An email classification scheme based on decision-theoretic rough set theory and analysis of email security, Proc. 2005 IEEE Region 10 TENCON, 2005, 2237-2242.
  • [25] Zhou, X. Z., Li, H. X.: A multi-view decision model based on decision-theoretic rough set, Proc. Rough Sets and Knowledge Technology (P. Wen, Y. Li, L. Polkowski, Y. Yao, S. Tsumoto, G. Wang, Eds.), LNCS (LNAI), 5589, Springer-Heidelberg, 2009, 650-657.
  • [26] Ziarko,W.: Variable precision rough set model, Journal of Computer and System Science, 46, 1993, 39-59.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0023-0043
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