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Dialogue in Hierarchical Learning of Concept using Prototypes and Counterexamples

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Języki publikacji
EN
Abstrakty
EN
This paper is an attempt to introduce a notion of dialogue among different agents, e.g. a user or situation descriptor and an information provider or machine, to better understand a user’s need of information. When a user describes a concept through some keywords first the system needs to choose the relevant attributes for initiating the search. Regarding this, a series of dialogues may help the system to better understand the user’s language of expresssion. After fixing the relevant attributes, the system needs to look at its repertoire of positive and negative cases of different concepts characterized with respect to this set of attributes. Then through a step-by-step process of matching and comparing similarity of user’s described situation with those positive and negative cases of a concept, incorporating several layers of dialogue, the system may provide information closer to the user’s need. The target of this paper is to develop a theoretical model, incorporating the above features, for characterizing one’s concern of concept based on dialogue and similarity based reasoning.
Wydawca
Rocznik
Strony
17--36
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
  • Vistula University, Warsaw, Poland
  • Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Poland
  • Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Poland
Bibliografia
  • [1] Bazan JG, Skowron A, and Swiniarski R. Layered learning for concept synthesis. Transactions on Rough Sets V: Journal Subline LNCS 4100, 2006 pp. 39-62. URL https://doi.org/10.1007/11847465_3.
  • [2] Chakraborty MK, and Banerjee M. Dialogue in rough context. In: LNAI 3066, Proc. Int. Conf. On Rough Sets and Current Trends in Computing (RSCTC 2004), Uppsala, Sweden, 2004, Eds. Tsumoto, S. et al. (Springer-Verlag), 2004 pp. 295-299. doi:10.1007/978-3-540-25929-9_34.
  • [3] Chakraborty MK, and Banerjee M. Rough dialogue and implication lattices. Fundamenta Informaticae, 2007;75(1-4):123-139. URL http://dl.acm.org/citation.cfm?id=2366376.2366382.
  • [4] Ciucci D. Orthopairs and Granular Computing, Granular Computing 2016;1:159-170. doi:10.1007/s41066-015-0013-y.
  • [5] Dubois D, and Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems, 1990;17(2-3):191-200. URL https://doi.org/10.1080/03081079008935107.
  • [6] Dubois D, Prade H. An introduction to bipolar representations of information and preference. Int. J. Intell. Syst. 23(8): 866-877 2008. URL https://doi.org/10.1002/int.20297.
  • [7] Dutta S, Wasilewski P. Concept synthesis using logic of prototypes and counterexamples: a graded consequence approach, M. Kryszkiewicz et al. (Eds.): PReMI 2015, LNCS 9124, 2015 pp. 303-313. doi:10.1007/978-3-319-19941-2_29.
  • [8] Godo L, Rodríguez RO. Logical approaches to fuzzy similarity-based reasoning: an overview, In Preferences an Similarities, Volume 504 of the series CISM International Centre for Mechanical Sciences 2008 pp. 75-128. doi:10.1007/978-3-211-85432-7_4.
  • [9] Klir GJ, and Yuan B. Fuzzy sets and fuzzy logic: theory and applications, Prentice Hall of India, 1995. ISBN-10:0131011715, 13:978-0131011717.
  • [10] Mitchell T. Generalization as Search, Artif Intell 1982;18:203-226.
  • [11] Nguyen SH, Bazan J, Skowron A, and Nguyen HS. Layered learning for concept synthesis. Transactions on Rough Sets I: Journal Subline LNCS 3100, 2004 pp. 187-208. doi:10.1007/978-3-540-27794-1_9.
  • [12] Pawlak Z, Skowron A. Rudiments of rough sets, Information Sciences, 2007;177(1):3-27. URL \https://doi.org/10.1016/j.ins.2006.06.003.
  • [13] Pawlak Z, Skowron A. Rough sets: Some extensions, Information Sciences, 2007;177(1):28-40. URL https://doi.org/10.1016/j.ins.2006.06.006.
  • [14] Pawlak Z, Skowron A. Rough sets and boolean reasoning, Information Sciences, 2007;177(1):41-73. URL https://doi.org/10.1016/j.ins.2006.06.007.
  • [15] Polkowski L, and Skowron A. Rough mereological approach to knowledge-based distributed AI, In Critical Technology, Proc. Third World Congress on Expert Systems, February 5-9, Soeul, Korea, J. K. Lee and J. Liebowitz and J. M. Chae (eds.), 774-781, Cognizant Communication Corporation, New York, 1996.
  • [16] Rahman S. Non-normal dialogics for a wonderful world and more, In J. van Benthem, G. Heinzmann, M. Rebuschi, and H. Visser (eds.), The Age of Alternative Logics: Assessing Philosophy of Logic and Mathematics Today, Dordrecht: Springer, 2002.
  • [17] Skowron A, and Stepaniuk J. Information Granules and Rough-Neural Computing, In Rough-Neural Computing: Techniques for Computing with Words, Sankar K. Pal, Lech Polkowski, and Andrzej Skowron (eds.), Series: Cognitive Technologies, 43-84, Springer-Verlag, Heidelberg, 2004. doi:10.1007/978-3-642-18859-6_3.
  • [18] Skowron A, and Stepaniuk J. Hierarchical modelling in searching for complex patterns: Constrained sums of information systems, Journal of Experimental and Theoretical Artificial Intelligence, 2005;17(1-2):83-102. URL https://doi.org/10.1080/09528130512331315873.
  • [19] Skowron A, Jankowski A, and Wasilewski P. Interactive computational systems: rough granular approach, Proceedings of the Workshop on Concurrency, Specification, and Programming (CS&P 2012), Humboldt University, Berlin, September 26-28, 2012, L. Popowa-Zeugmann (ed.), Informatik-Bericht series, Vol 225, 2012 pp. 358-369. doi:10.1016/j.tcs.2012.04.019.
  • [20] Walton DN, Krabbe ECW (eds.). Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning, State University of New York Press, Albany, 1995. ISBN-10:079142586X, 13:978-0791425862.
  • [21] Yao Y. Interval Sets and Interval-Set Algebras, In: Proceedings of the 8th IEEE International Conference on Cognitive Informatics, 2009 pp. 307-314. doi:10.1109/COGINF.2009.5250723
Typ dokumentu
Bibliografia
Identyfikator YADDA
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