Rough Set Based Reasoning About Changes

• Autorzy: Skowron A. , Stepaniuk J. , Jankowski A. , Bazan J.G. , Swiniarski R.
• Języki publikacji: EN

Abstrakty

EN

We consider several issues related to reasoning about changes in systems interacting with the environment by sensors. In particular, we discuss challenging problems of reasoning about changes in hierarchical modeling and approximation of transition functions or trajectories. This paper can also be treated as a step toward developing rough calculus.

Słowa kluczowe

EN

rough sets; reasoning about changes; hierarchical modeling; granular computing; approximation space; relation approximation; rough calculus; intelligent systems; computational finance; Forex; algorithmic trading

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2012
• Tom: Vol. 119, nr 3/4
• Strony: 421--437
• Opis fizyczny: Bibliogr. 46 poz., tab., wykr.

Twórcy

autor

Skowron A.

autor

Stepaniuk J.

autor

Jankowski A.

autor

Bazan J.G.

autor

Swiniarski R.

• Institute of Mathematics, The University of Warsaw, Banacha 2, 02-097 Warszawa, Poland,

Bibliografia

• [1] Burgin, M.: Neoclassical Analysis: Calculus Closer to the Real World. Nova Science Publishers, Inc. New York, 2007.
• [2] Fisher, M., Gabbay, D., Vila, L. (eds.): Handbook of Temporal Reasoning in Artificial Intelligence, vol. 1. Elsevier, Amsterdam, 2005.
• [3] Gabbay, D. M., Hogger, C. J., and Robinson, J. A. (eds.): Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 3, Nonmonotonic Reasoning and Uncertain Reasoning. Oxford: Oxford University Press, Oxford UK, 1994.
• [4] Gabbay, D. M., Schlechta, K.: Logical Tools for Handling Change in Agent-Based Systems. Springer, Heidelberg, 2010.
• [5] Hastie, T., Tibshirani, R., Friedman, J. H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, Heidelberg, 2008.
• [6] Hüllermeier, E.: Case-Based Approximate Reasoning. Springer, Dordrecht, 2007.
• [7] Jankowski, A., Skowron, A.: A wistech paradigm for intelligent systems. LNCS Journal Subline: Transactions on Rough Sets VI: Journal Subline, LNCS 4374 (2007) 94-132, Springer, Heidelberg.
• [8] Jankowski, A., Skowron, A.: Logic for artificial intelligence: The Rasiowa - Pawlak school perspective. In: A. Ehrenfeucht, V. Marek, M. Srebrny (eds.) Andrzej Mostowski and Foundational Studies, IOS Press, Amsterdam, 2008, 106-143.
• [9] Jankowski, A., Skowron, A.: Wisdom technology: A rough-granular approach. In: M. Marciniak, A. Mykowiecka (eds.) Bolc Festschrift, LNCS 5070, Springer, Heidelberg, 2009, 3-41.
• [10] Kowalski, R. A., Sergot, M.J.: A logic-based calculus of events. New Generation Computing 4(1) (1986) 67-95.
• [11] Kuipers, B.: Qualitative reasoning: Modeling and simulation with incomplete knowledge. MIT Press, Cambridge, MA, 1994.
• [12] McCarthy, J.: Situations, actions and causal laws (Memo 2). Stanford Artificial Intelligence Project, Stanford University, CA 1963.
• [13] McCarthy, J.,Hayes, P. J.: Some philosophical problems from the standpoint of artificial intelligence. In: B. Meltzer, D. Michie (eds.) Machine Intelligence 4, Edinburgh University Press, Edinburgh, Scotland, 1969, 463-502.
• [14] Mueller, E. T.: Commonsense Reasoning. Morgan Kaufmann, Elsevier, San Francisco CA, 2006.
• [15] Nguyen, H. S.: Approximate Boolean reasoning: Foundations and applications in data mining. Transactions on Rough Sets V: Journal Subline, LNCS 3100 (2007) 334-506, Springer, Heidelberg.
• [16] Nguyen, H. S., Skowron, A., Stepaniuk, J.: Discovery of changes along trajectories generated by process models induced from data and domain knowledge. In: G. Lindemann, H.-D. Burkhard, L. Czaja,W. Penczek, A, Salwicki, H. Schlingloff, A. Skowron, Z. Suraj (eds.) Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P 2008), vol. 1-3, Gross Vaeter, Germany, September 29-October 1, 2008, Humboldt Universitaet zu Berlin, Informatik-Berichte, Berlin, 2008, 350-362.
• [17] Nguyen, H.S., Jankowski, A., Peters, J. F., Skowron, A., Stepaniuk, J., Szczuka, M.: Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach. In: J.T. Yao (ed.) Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, IGI Global, Hershey, New York, 2010, 16-47.
• [18] Noë, A.: Action in Perception. MIT Press, Cambridge, MA, 2004.
• [19] Pawlak Z.: Rough functions. ICS PAS Reports 467/81, Institute of Computer Science Polish Academy of Sciences (ICS PAS), Warsaw, Poland, 1981, 1-11.
• [20] Pawlak Z.: Rough relations. ICS PAS Reports 435/81, Institute of Computer Science Polish Academy of Sciences (ICS PAS), Warsaw, Poland, 1981, 1-10.
• [21] Pawlak Z.: On rough relations. Bulletin of the Polish Academy of Sciences, Technical Sciences 34(9-10) (1986) 587-590.
• [22] Pawlak Z. On rough functions. Bulletin of the Polish Academy of Sciences, Technical Sciences 35(5-6) (1987) 249-251.
• [23] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving, vol. 9, Kluwer Academic Publishers, Dordrecht, 1991.
• [24] Pawlak, Z.: Rough calculus. In: Proceedings of the Second Annual Joint Conference on Information Sciences, September 28 - October 1, 1995, Wrightsville Beach, NC, USA, 1995, 344-345.
• [25] Pawlak, Z.: Rough sets, rough functions and rough calculus. In: S.K. Pal and A. Skowron (eds.), Rough Fuzzy Hybridization, A New Trend in Decision Making, Springer-Verlag, Singapore 1999, 99-109.
• [26] Pawlak, Z., Skowron, A.: Rudiments of rough sets; Rough sets: Some extensions; Rough sets and Boolean reasoning. Information Sciences 177(1) (2007) 3-27; 28-40; 41-73.
• [27] Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, New York, 2008.
• [28] Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.): Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56. Springer-Verlag/Physica-Verlag, Heidelberg, 2000.
• [29] Ramsay J. O., Silverman B. W.: Applied Functional Data Analysis. Springer, Berlin, 2002.
• [30] Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. The MIT Press, Cambridge MA, 2001.
• [31] Sandewall, E.: Features and Fluents. Oxford University Press, Oxford UK, 1994.
• [32] Shoham, Y.: Reasoning About Change: Time and Causation from the Standpoint of Artificial Intelligence. MIT Press, Cambridge, MA, 1988.
• [33] Skowron, A., Stepaniuk, J.: Approximation spaces in rough-granular computing. Fundamenta Informaticae 100 (2010) 141-157.
• [34] Skowron, A., Stepaniuk J.: Data driven approximate reasoning about changes, In: M. Szczuka, L. Czaja, A. Skowron, M. Kacprzak (eds.) Concurrency, Specification and Programming : CS&P'2011: International Workshop, Pultusk, 28 - 30 September 2011, Białystok University of Technology, Humboldt University, Warsaw University, 2011, 477-486.
• [35] Skowron, A., Stepaniuk, J., Swiniarski, R.: Modeling rough granular computing based on approximation spaces. Information Sciences 184 (2012) 20-43.
• [36] Skowron A., Stepaniuk J., Peters J., Swiniarski R.: Calculi of approximation spaces. Fundamenta Informaticae 72(1-3) (2006) 363-378.
• [37] Skowron, A., Szczuka, M.: Toward interactive computations: A rough-granular approach. In: J. Koronacki, S. Wierzchon, Z. Ras, J. Kacprzyk (eds.), Commemorative Volume to Honor Ryszard Michalski. Springer, Heidelberg, 2009, 1-20.
• [38] Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. Theoretical Computer Science 412(42) (2011) 5939-5959.
• [39] Stepaniuk J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. Springer, Heidelberg, 2008.
• [40] Suraj, Z.: Rough set methods for the synthesis and analysis of concurrent processes. In: Polkowski et al. [28], 379-488.
• [41] Szczuka, M., Skowron, A., Stepaniuk, J.: Function approximation and quality measures in rough-granular systems. Fundamenta Informaticae 109(3-4) (2011) 339-354.
• [42] Ślęzak, D., Wróblewski, J.: Roughfication of numeric decision tables: The case study of gene expression data. In: Yao, J. T., Lingras, P., Wu, W.-Z., Szczuka, M., Cercone, C., Ślęzak, D. (eds.), Proceedings of the Second International Conference on Rough Sets and Knowledge Technology (RSKT 2007), Toronto, Canada, May 14-16, 2007, LNCS 4481, Springer, Heidelberg, 2007, 316-323.
• [43] van Harmelen, F., Lifschitz, V., Porter, B. (Eds.): Handbook of Knowledge Representation. Elsevier, Amsterdam, 2008.
• [44] van der Aalst, W.M.P. (ed.): Process Mining Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg, 2011.
• [45] Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York, NY (1998)
• [46] Wnek, J., Michalski, R. S.: Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning 14(1) (1994) 139-168