Multiagent Approach to Fuzzy-Linguistic Knowledge Integration

• Autorzy: Modliński Ł. , Popek G.

Identyfikatory

DOI

10.12921/cmst.2018.0000047

• Języki publikacji: EN

Abstrakty

EN

The paper aims to give at least a partial answer to an urgent need for knowledge processing systems equipped with semantic capabilities. One of the crucial goals is to reflect inner computational models and numerical data outside of the system by presenting linguistic statements easily understood by a non-expert user. The paper follows a motivational scenario and presents a layered approach to knowledge integration. The fundamental rationale behind the proposed approach is that a degree of inconsistency of the whole body of knowledge should be incorporated into the formed summary and conveyed to the external user of the system. The paper deals with a practically important problem of processing modal epistemic statements about an object exhibiting some set of fuzzy properties. The statements represent distributed knowledge of some agent population and are represented on the level of a semi-natural language. In particular, the paper describes an approach to two-level fuzzy-linguistic knowledge integration based on the consensus-theory and clustering methods. In particular, it discusses the difference between the in-cluster level and the cross-cluster level. While this paper considers an environment limited to a single object with multiple properties, it is directly extendable to environments with multiple objects. The reduction is purely technical as it allows for a simplification of a notation and presented descriptions.

Słowa kluczowe

EN

knowledge integration; fuzzy-linguistic variable; artificial agents; multi-agent systems; semantic communication

• Wydawca: Institute of Bioorganic Chemistry Scientific Publishers OWN, Polish Academy of Sciences
• Czasopismo: Computational Methods in Science and Technology
• Rocznik: 2018
• Tom: Vol. 24, No. 4
• Strony: 317--336
• Opis fizyczny: Bibliogr. 79 poz., rys.

Twórcy

autor

Modliński Ł.

• Wrocław University of Science and Technology Faculty of Computer Science and Management, Department of Computer Science Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Popek G.

• Wrocław University of Science and Technology Faculty of Computer Science and Management, Department of Computer Science Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• [1] C. Luo, A.P. Espinosa, D. Pranantha, A. De Gloria, Multirobot search and rescue team, in Safety, Security, and Rescue Robotics (SSRR), 2011 IEEE International Symposium on. IEEE, 2011, pp. 296–301.
• [2] S.B. Williams, O. Pizarro, I. Mahon, M. Johnson-Roberson, “Simultaneous localisation and mapping and dense stereoscopic seafloor reconstruction using an auv,” in Experimental robotics, Springer, pp. 407–416 (2009).
• [3] A.Jungmann,B.Kleinjohann,W.Richert,Increasinglearning speed by imitation in multi-robot societies, in Organic Computing?A Paradigm Shift for Complex Systems, Springer, pp. 295–307, 2011.
• [4] S.Harnad,Thesymbolgroundingproblem,PhysicaD:Nonlinear Phenomena 42(1), pp. 335–346 (1990).
• [5] G.Popek,Integrationofmodalandfuzzymethodsforagent’s knowledge representation, Ph.D. dissertation, Swinburne University of Technology, VIC, Australia, Wroclaw University of Technology, Poland, 2013.
• [6] R.Babuška,Constructionoffuzzysystems-interplaybetween precision and transparency, Proc. ESIT 2000, pp. 445–452, 2000.
• [7] M.Markovic,Dialecticaltheoryofmeaning.SpringerScience & Business Media, 81, 2012.
• [8] R.R. Yager, On linguistic summaries of data, Knowledge discovery in databases, pp. 347–363, 1991.
• [9] A.Niewiadomski,Atype-2fuzzyapproachtolinguisticsummarization of data, Fuzzy Systems, IEEE Transactions on 16(1), pp. 198–212 (2008).
• [10] C.-H. Wang, T.-P. Hong, S.-S. Tseng, A genetics-based approach to knowledge integration and refinement, Journal of Information Science and Engineering 17(1), pp. 85–94 (2001).
• [11] C.-H. Wang, T.-P. Hong, M.-B. Chang, S.-S. Tseng, A coverage-based genetic knowledge-integration strategy, Expert Systems with Applications 19(1), pp. 9–17 (2000).
• [12] C.-H.Wang,T.-P.Hong,S.-S.Tseng,Integratingmembership functions and fuzzy rule sets from multiple knowledge sources, Fuzzy Sets and Systems 112(1), pp. 141–154 (2000).
• [13] M.Maleszka,B.Mianowska,N.T.Nguyen,“Amethodforcollaborative recommendation using knowledge integration tools and hierarchical structure of user profiles,” Knowledge-Based Systems 47, pp. 1–13 (2013).
• [14] K.C. Lee, N. Lee, H. Lee, Multi-agent knowledge integration mechanism using particle swarm optimization, Technological Forecasting and Social Change 79(3), pp. 469–484 (2012).
• [15] B.Kosko,Fuzzycognitivemaps,Internationaljournalofmanmachine studies 24(1), pp. 65–75 (1986).
• [16] R.C.EberhartandJ.Kennedy,Anewoptimizerusingparticle swarm theory, in Proceedings of the sixth international symposium on micro machine and human science 1. New York, NY, 1995, pp. 39–43.
• [17] J.Kennedy,Particleswarmoptimization,inEncyclopediaof Machine Learning. Springer, 2010, pp. 760–766.
• [18] C.-H.Wang,T.-P.Hong,S.-S.Tseng,Integratingfuzzyknowledge by genetic algorithms, Evolutionary Computation, IEEE Transactions on, 2(4), pp. 138–149 (1998).
• [19] B.Jankowska,Usingsemanticdataintegrationtocreatereliable rule-based systems with uncertainty, Engineering Applications of Artificial Intelligence 24(8), pp. 1499–1509 (2011).
• [20] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning—i, Information sciences 8(3), pp. 199–249 (1975).
• [21] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning—ii, Information sciences 8(4), pp. 301–357 (1975).
• [22] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning-iii, Information sciences 9(1), pp. 43–80 (1975).
• [23] R. Fagin, J.Y. Halpern, Y. Moses, M.Y. Vardi, Reasoning about knowledge, MIT press Cambridge, 1995, vol. 4.
• [24] D. Dubois and H. Prade, On the validation of fuzzy knowledge bases, in Fuzzy Reasoning in Information, Decision and Control Systems, Springer, 1994, pp. 31–49.
• [25] H. Bandemer, Fuzzy local inference in fuzzy knowledge bases, in Fuzzy Approach to Reasoning and Decision-Making, Springer, 1992, pp. 39–49.
• [26] A. Niewiadomski, Methods for the Linguistic Summarization of Data: Aplications of Fuzzy Sets and Their Extensions, Akademicka Oficyna Wydawnicza Exit, 2008.
• [27] N.M. Bigolin and C. Marsala, Fuzzy spatial oql for fuzzy knowledge discovery in databases, in Principles of Data Mining and Knowledge Discovery. Springer, 1998, pp. 246–254.
• [28] E. Reiter, S. Sripada, J. Hunter, J. Yu, I. Davy, Choosing words in computer-generated weather forecasts, Artificial Intelligence, 167(1), pp. 137–169 (2005).
• [29] G.Popek,R.Kowalczyk,R.P.Katarzyniak,Introducingfuzzy labels to agent-generated textual descriptions of incomplete city-traffic states, in Computational Collective Intelligence. Technologies and Applications. Springer, 2012, pp. 550–561.
• [30] R.R. Yager, A new approach to the summarization of data, Information Sciences 28(1), pp. 69–86 (1982).
• [31] R.P.Katarzyniak,Groundingcrispandfuzzyontologicalconcepts in artificial cognitive agents, in Knowledge-Based Intelligent Information and Engineering Systems. Springer, 2006, pp. 1027–1034.
• [32] J. Lang and P. Marquis, Removing inconsistencies in assumption-based theories through knowledge-gathering actions, Studia Logica 67(2), pp. 179–214 (2001).
• [33] J. Nuyts, Epistemic modality, language, and conceptualization: A cognitive-pragmatic perspective. John Benjamins Publishing, 2001 5.
• [34] R.P. Katarzyniak and A. Pieczyn ́ska-Kuchtiak, “Grounding and extracting modal responses in cognitive agents:’and’query and states of incomplete knowledge,” International Journal of Applied Mathematics and Computer Science 14, pp. 249–263 (2004).
• [35] L. Padgham and M. Winikoff, Developing intelligent agent systems: A practical guide. John Wiley & Sons, 2005 13.
• [36] T.B. Klos and J. La Poutré, Decentralized reputation-based trust for assessing agent reliability under aggregate feedback, Software Engineering [SEN], no. E 0422, pp. 1–23, 2004.
• [37] M.Lewicka,Confirmationbias,inPersonalControlinAction. Springer, 1998, pp. 233–258.
• [38] W.LorkiewiczandR.P.Katarzyniak,Recallingtheembodied meaning of modal conjunctions in artificial cognitive agents, in Agent and Multi-Agent Systems: Technologies and Applications. Springer, 2008, pp. 763–772.
• [39] W.Lorkiewicz,R.Kowalczyk,R.Katarzyniak,Q.B.Vo,On topic selection strategies in multi-agent naming game, in The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 2. International Foundation for Autonomous Agents and Multiagent Systems, 2011, pp. 499– 506.
• [40] L. Steels and M. Loetzsch, The grounded naming game, Experiments in cultural language evolution. Amsterdam: John Benjamins, 2012.
• [41] P. Vogt, Perceptual grounding in robots, lecture notes on artificial intelligence, in Proceedings of the 6th European Workshop on Learning Robots, Lecture notes on Artificial Intelligence. Springer, 1998.
• [42] S. Franklin and A. Graesser, Is it an agent, or just a program?: A taxonomy for autonomous agents, in Intelligent agents III agent theories, architectures, and languages Springer, 1997, pp. 21–35.
• [43] G. Weiss, Multiagent systems: a modern approach to distributed artificial intelligence. MIT press, 1999.
• [44] S. Russell, P. Norvig, A. Intelligence, A modern approach, Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs 25, 1995.
• [45] J. Huang, N.R. Jennings, J. Fox, An agent architecture for distributed medical care, in Intelligent Agents. Springer, 1995, pp. 219–232.
• [46] C.Adam,R.Canal,B.Gaudou,H.T.Vinh,P.Taillandieretal.,Simulation of the emotion dynamics in a group of agents in an evacuation situation, in Principles and Practice of MultiAgent Systems. Springer, 2012, pp. 604–619.
• [47] J.P.Müller,M.Pischel,M.Thiel,Modelingreactivebehaviour in vertically layered agent architectures, in Intelligent Agents. Springer, 1995, pp. 261–276.
• [48] J.P. Müller and M. Pischel, The agent architecture interrap: Concept and application, 2011.
• [49] J.P. Müller, The design of intelligent agents: a layered approach, Springer Science & Business Media, 1996, 1177.
• [50] I.A.Ferguson,Touringmachines:Anarchitecturefordynamic, rational, mobile agents, Ph.D. dissertation, University of Cambridge Cambridge, 1992.
• [51] F. Dignum and M. Greaves, Issues in agent communication, Springer Science & Business Media, 2000, no. 1916.
• [52] A.Fipa,Fipaaclmessagestructurespecification,Foundation for Intelligent Physical Agents, http://www. fipa. org/specs/- fipa00061/SC00061G. html (30.6. 2004), 2002.
• [53] R.Katarzyniak,Thelanguagegroundingproblemanditsrelation to the internal structure of cognitive agents. J. UCS 11(2), pp. 357–374 (2005).
• [54] M.TaddeoandL.Floridi,Solvingthesymbolgroundingproblem: a critical review of fifteen years of research, Journal of Experimental & Theoretical Artificial Intelligence 17(4), pp. 419–445 (2005).
• [55] F.Dignum,AdvancesinAgentCommunication:International Workshop on Agent Communication Languages ACL 2003, Melbourne, Australia, July 14, 2003, Springer Science & Business Media, 2004, 2922.
• [56] J.ToháandM.Soto,Onthedeterminationofinternalnodes of an evolutionary dendrogram, Origins of Life and Evolution of the Biosphere 28(1), pp. 97–103 (1998).
• [57] C. Cachin, R. Guerraoui, L. Rodrigues, Introduction to reliable and secure distributed programming, Springer Science & Business Media, 2011.
• [58] W. Lorkiewicz, G. Popek, R.P. Katarzyniak, Intuitive approach to knowledge integration, in Human System Interaction (HSI), 2013 The 6th International Conference on, IEEE, 2013, pp. 40–47.
• [59] M. Majewski and W. Kacalak, Natural language humanmachine interface using artificial neural networks, in Advances in Neural Networks-ISNN 2006, Springer, 2006, pp. 1161–1166.
• [60] B. D’Ambrosio, Qualitative process theory using linguistic variables, Springer Science & Business Media, 2012.
• [61] T. Matsuka and Y. Sakamoto, A cognitive model of concept learning with a flexible internal representation system, in Advances in Neural Networks–ISNN 2007, Springer, 2007, pp. 1135–1143.
• [62] R.A. Brooks, Intelligence without representation, Artificial intelligence 47(1), pp. 139–159 (1991).
• [63] K. Kuratowski, A. Mostowski, M. Maczynski, Set theory, North-Holland Amsterdam, 1968 48.
• [64] N.T. Nguyen, Metody wyboru consensusu i ich zastosowanie w rozwiazywaniu konfliktow w systemach rozproszonych, Oficyna Wydawnicza Politechniki Wroclawskiej, 2002.
• [65] H.R.F.JuanCarlosFigueroa-Garci?aa,YurilevChalco-Canob, Distance measures for interval type-2 fuzzy numbers, in Discrete Applied Mathematics, Elsvier, 2014.
• [66] G. Skorupa, M.L. Katarzyniak, Radoslaw Piotr, M. Mulka, Multi-agent platform for fuzzy structures integration task, ISAT, 2013.
• [67] F.d.A.deCarvalho,Afuzzyclusteringalgorithmforsymbolic interval data based on a single adaptive euclidean distance, in Neural Information Processing. Springer, 2006, pp. 1012– 1021.
• [68] N.T.Nguyen,Advancedmethodsforinconsistentknowledge management. Springer, 2007.
• [69] K.LehrerandC.Wagner,RationalConsensusinScienceand Society: A Philosophical and Mathematical Study. Springer Science & Business Media 21. (1981).
• [70] K.C.Bausch,Theemergingconsensusinsocialsystemstheory. Springer Science & Business Media, 2001.
• [71] D. Ongaro and J. Ousterhout, In search of an understandable consensus algorithm, in Proc. USENIX Annual Technical Conference, 2014, pp. 305–320.
• [72] A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: a review, ACM computing surveys (CSUR) 31(3), pp. 264–323 (1999). [73] G. Uchyigit and K. Clark, Hierarchical agglomerative clustering for agent-based dynamic collaborative filtering, in Intelligent Data Engineering and Automated Learning–IDEAL 2004. Springer, 2004, pp. 827–832.
• [74] L.Modlinski and G.Popek,Representingresultofknowledge integration with modal linguistic statements, ISAT, 2014. [75] P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of computational and applied mathematics 20, pp. 53–65 (1987).
• [76] R.Tibshirani,G.Walther,T.Hastie,Estimatingthenumberof clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63(2), pp. 411–423, 2001.
• [77] A.D.Gordon,Nullmodelsinclustervalidation,inFromdata to knowledge. Springer, 1996, pp. 32–44.
• [78] N.J. Gotelli and B.J. McGill, Null versus neutral models: what’s the difference? Ecography 29(5), pp. 793–800 (2006).
• [79] R.R. Yager, K.M. Ford, A.J. Cañas, An approach to the linguistic summarization of data, in Uncertainty in Knowledge Bases. Springer, 1991, pp. 456–468.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).