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Complex Decision Systems and Conflicts Analysis Problem

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Języki publikacji
EN
Abstrakty
EN
This paper discusses the issues related to the conflict analysis method and the rough set theory, process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The value of the rough set theory and conflict analysis applied in practical decision support systems with complex domain knowledge are expressed. The furthermore examples of decision support systems with complex domain knowledge are presented in this article. The paper proposes a new approach to the organizational structure of a multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of knowledge inconsistencies elimination will be implemented for created groups. Global decisions will be made using one of the methods for analysis of conflicts.
Wydawca
Rocznik
Strony
341--356
Opis fizyczny
Bibliogr. 28 poz., tab., wykr.
Twórcy
  • Institute of Computer Science, University of Silesia, Poland
  • Institute of Computer Science, University of Silesia, Poland
  • Institute of Computer Science, University of Silesia, Poland
Bibliografia
  • [1] Bazan, J., Peters, J., Skowron, A., Nguyen, H., Szczuka, M.: Rough set approach to pattern extraction from classifiers, In: Electronic Notes in Theoretical Computer Science 82, Elsevier Science Publishers, 2003.
  • [2] Deja, R.: Conflict Analysis, Rough Sets; New Developments, In: Polkowski, L. (eds.) Studies in Fuzziness and Soft Computing, Physical-Verlag, 2000.
  • [3] Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, In: Proc. of 2nd Int. Con. on Knowledge Discovery and Data Mining, 226231, 1996.
  • [4] Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data, Proceedings of The 9th International Conference on Information Processing and Management of Uncertainty in Knowledge- Based Systems, IPMU 2002, Annecy, France, July 1-5, 243-250,2002.
  • [5] Ilczuk, G., Wakulicz-Deja, A.: Rough Sets Approach to Medical Diagnosis System, Lecture Notes in Computer Science, Advances in Web Intelligence, Springer-Verlag, 204-210,2005.
  • [6] Ilczuk, G., Mlynarski, R., Wakulicz-Deja, A.,Drzewiecka, D., Kargul, W.: Rough set techniques for medical diagnosis systems, IEEE Computers in Cardiology, Cardiology, 375-378,2005.
  • [7] Kuncheva, L.: Combining Pattern Classifiers Methods and Algorithms, John Wiley & Sons, 2004.
  • [8] Lopes, F., Wooldridge, M., Novais, A.Q.: Negotiation among autonomous computational agents: principles, analysis and challenges, Artificial Intelligence Review Vol. 29, No. 1, 1-44, 2008.
  • [9] Nguyen, T., Jennings, N.: Coordinating multiple concurrent negotiations, In Proc. of 3rd Int. J. Conf. on Autonomous Agents and Multi Agent Systems, New York, USA, 1064-1071,2004.
  • [10] Nowak, A., Wakulicz-Deja, A.: The way of rules representation in composited knowledge bases, Advanced In Intelligent and Soft Computing, Man - Machine Interactions, Springer-Verlag, 175-182,2009.
  • [11] Nowak, A., Wakulicz-Deja, A.: The concept of the hierarchical clustering algorithms for rules based systems, Springer Verlag - Advances in Soft Computing, Springer-Verlag, 565-570, 2005.
  • [12] Nowak, A., Wakulicz-Deja, A.: The inference processes on composited knowledge bases, Control and Cybernetics, Intelligent Information Systems 2008, ISBN 978-83-60434-44-4,415-422, 2008.
  • [13] Nowak-Brzezinska, A., Wakulicz-Deja, A., Jach, T.: Inference Processes Using Incomplete Knowledge in Decision Support Systems-Chosen Aspects, Lecture Notes in Computer Science, Vol. 7413, Rough Sets and Current Trends in Computing, Springer-Verlag, 150-155,2012.
  • [14] Paszek, P., Wakulicz-Deja, A.: Diagnose progressive encephalopathy applying the rough set theory, International journal of medical informatics, volume 46, issue 2, 119-127, 1997.
  • [15] Paszek, P., Wakulicz-Deja, A.: Applying Rough Set Theory to Multi Stage Medical Diagnosing, Fundamenta Informaticae, Volume 54, issue 4, 387-408,2003.
  • [16] Pawlak, Z.: On Conflicts, Int. J. of Information and Computer Science 11, 344-356, 1984.
  • [17] Pawlak, Z.: O konfliktach, Państwowe Wydawnictwo Naukowe, Warszawa, 1987.
  • [18] Pawlak, Z.: An Inquiry Anatomy of Conflicts, Journal of Information Sciences 109,65-78, 1998.
  • [19] Polkowski, L., Araszkiewicz, B.: A rough set approach to estimating the game value and the Shapley value from data, Fundamenta Informaticae 53, 335-343, 2003.
  • [20] Przybyla-Kasperek, M., Wakulicz-Deja, A.: Application of decision rules, generated on the basis of local knowledge bases, in the process of global decision-making, 4th Int. Symp. on Intelligent Decision Technologies, IDT-2012, INVITED SESSION Rough Sets and Granular Computing, 2012.
  • [21] Przybyla-Kasperek, M., Wakulicz-Deja, A.: Application of reduction of the set of conditional attributes in the process of global decision-making, Fundamenta Informaticae 122 (4), 327-355,2013.
  • [22] Skowron, A., Deja, R.: On Some Conflict Models and Conflict Resolutions, Romanian Journal of Information Science and Technology 3(1-2), 69-82, 2002.
  • [23] Skowron, A., Rauszer, C. : The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support, Handbook of Advances and Applications of the Rough Set Theory, Kluwer Academic Publishers, 331-362. 1992.
  • [24] Slezak, D., Wroblewski, J., Szczuka, M.: Neural network architecture for synthesis of the probabilistic rule based classifiers, In: Electronic Notes in Theoretical Computer Science 82, Elsevier, 2003.
  • [25] Wakulicz-Deja, A., Przybyla-Kasperek, M.: Global decisions Taking on the Basis of Multi-Agent System with a Hierarchical Structure and Density-Based Algorithm, CS&P, Uniwersytet Warszawski, 616-627, 2009.
  • [26] Wakulicz-Deja, A., Przybyla-Kasperek, M.: Multi-Agent Decision Taking System, Fundamenta Informaticae 101(1-2), 125-141, 2010.
  • [27] Wakulicz-Deja, A., Przybyla-Kasperek, M.: Application of the method of editing and condensing in the process of global decision-making, Fundamenta Informaticae 106 (1), 93-117,2011.
  • [28] Zeng, D., Sycara, K.: Bayesian learning in negotiation, Int J Hum Comput Stud 48, 125-141, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-81e51196-08f2-4985-a488-9c455b18f478
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