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Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 1))

Abstract

This chapter presents some ideas that concern a pattern of human knowledge. This pattern is based on the experimentation of causal relations. The cultural origin of the patterns is analyzed in terms of philosophical, psychological and linguistic points of view. An application scenario related to a robot integrated in a cognitive system is described. The definitions of signatures and of signature classes are given as useful steps in an alternative modeling approach to the observation process.

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References

  1. Griffiths, T.L., Kemp, C., Tenenbaum, J.B.: Bayesian models of cognition. In: Sun, R. (ed.) The Cambridge Handbook of Computational Psychology, pp. 59–100. Cambridge University Press, Cambridge (2008)

    Google Scholar 

  2. Pozna, C., Precup, R.E.: A new pattern of knowledge based on experimenting the causality relation. In: Proceedings of 14th International Conference on Intelligent Engineering Systems (INES 2010), Las Palmas, Spain, pp. 61–66 (2010)

    Google Scholar 

  3. Kóczy, L.T., Vámos, T., Biró, G.: Fuzzy signatures. In: Proceedings of 4th Meeting of the Euro Working Group on Fuzzy Sets and the 2nd International Conference on Soft and Intelligent Computing (EUROPUSE-SIC 1999), Budapest, Hungary, pp. 210–217 (1999)

    Google Scholar 

  4. Vámos, T., Kóczy, L.T., Biró, G.: Fuzzy signatures in data mining. In: Proceedings of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada, vol. 5, pp. 2842–2846 (2001)

    Google Scholar 

  5. Wong, K.W., Gedeon, T.D., Kóczy, L.T.: Construction of fuzzy signature from data: An example of SARS pre-clinical diagnosis system. In: Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004), Budapest, Hungary, pp. 1649–1654 (2004)

    Google Scholar 

  6. Wong, K.W., Gedeon, T.D., Kóczy, L.T.: Fuzzy signature and cognitive modelling for complex decision model. In: Castillo, O., Melin, P., Montiel Ross, O., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds.) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. AISC, vol. 42, pp. 380–389. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Hadad, A.H., Gedeon, T.D., Mendis, B.S.U.: Finding input sub-spaces for polymorphic fuzzy signatures. In: Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2009), Jeju Island, Korea, pp. 1089–1094 (2009)

    Google Scholar 

  8. Ballagi, Á., Kóczy, L.T., Gedeon, T.D.: Robot cooperation without explicit communication by fuzzy signatures and decision trees. In: Proceedings of Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT 2009), Lisbon, Portugal, pp. 1468–1473 (2009)

    Google Scholar 

  9. http://www.fp6.cordis.lu/fp6/call

  10. McClelland, J.L., Rumelhart, D.E.: The PDP research group: parallel distributed processing: Explorations in the microstructure of cognition, Psychological and biological models, vol. 2. MIT Press, Cambridge (1986)

    Google Scholar 

  11. Broadbent, D.E.: Perception and communication. Pergamon Press, London (1958)

    Book  Google Scholar 

  12. Treisman, M., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12, 97–136 (1980)

    Article  Google Scholar 

  13. Johnston, W.A., Wilson, J.: Perceptual processing of nontargets in an attention task. Mem. Cogn. 8, 372–377 (1980)

    Article  Google Scholar 

  14. Malim, T.: Cognitive processes. Editura Tehnica, Bucharest (1999) (in Romanian)

    Google Scholar 

  15. Lindsay, P.H., Norman, D.A.: Human information processing: An introduction to psychology. Academic Press, New York (1972)

    Google Scholar 

  16. Biederman, I.: Recognition by components: A theory of human image understanding. Psychol. Rev. 94, 115–147 (1987)

    Article  Google Scholar 

  17. Neisser, U.: Memory observed: remembering in natural contexts. W. H. Freeman, San Francisco (1982)

    Google Scholar 

  18. Atkinson, R.C., Rauch, M.G.: An application of the mnemonic keyword method to the learning of a Russian vocabulary. J. Exp. Psychol. Hum. Learn. Mem. 1, 126–133 (1975)

    Article  Google Scholar 

  19. Baddeley, D.: The fractionation of human memory. Psychol. Med. 14, 259–264 (1984)

    Article  Google Scholar 

  20. Levine, M.A.: A cognitive theory of learning. Lawrence Erlbaum, Hillsdale (1985)

    Google Scholar 

  21. McNeill, D.: The creation of language. In: Oldfield, R.C., Marshall, J.C. (eds.) Language, pp. 21–31. Penguin Books, London (1968)

    Google Scholar 

  22. Yoshida, E., Kokaji, S., Murata, S., Tomita, K., Kurokawa, H.: Miniaturization of self-reconfigurable robotic system using shape memory alloy actuators. J. Robot Mechatronics 12, 96–102 (2000)

    Google Scholar 

  23. Chater, N., Tenenbaum, J.B., Yuille, A.: Theory-based Bayesian models of inductive learning and reasoning. Trend Cogn. Sci. 10, 309–318 (2006)

    Article  Google Scholar 

  24. Pozna, C.: An introduction on cognition system design. Acta Polytechnica Hungarica 4, 33–47 (2007)

    Google Scholar 

  25. Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robot Auton. Syst. 57, 75–86 (2009)

    Article  Google Scholar 

  26. Hladek, D., Vaščák, J., Sinčák, P.: Multi-robot control system for pursuit-evasion problem. J. Electr. Eng. 60, 143–148 (2009)

    Google Scholar 

  27. Hermann, G., Kozlowsky, K.R., Tar, J.K.: Design of a planar high Precision Motion Stage. In: Kozlowsky, K.R. (ed.) Robot Motion and Control 2009. LNCIS, vol. 396, pp. 371–379 (2009)

    Google Scholar 

  28. Wang, Y., Lang, H., de Silva, C.W.: A hybrid visual servo controller for robust grasping by wheeled mobile robots. IEEE/ASME Trans. Mechatronics 13, 757–769 (2010)

    Article  Google Scholar 

  29. Skoglund, A., Iliev, B., Palm, R.: Programming-by-demonstration of reaching motions - A next-state-planner approach. Robot Auton. Syst. 58, 607–621 (2010)

    Article  Google Scholar 

  30. Vaščák, J., Madarász, L.: Adaptation of fuzzy cognitive maps - a comparison study. Acta Polytechnica Hungarica 7, 109–122 (2010)

    Google Scholar 

  31. Klančar, G., Matko, D., Blažič, S.: A control strategy for platoons of differential-drive wheeled mobile robot. Robot Auton. Syst. 57, 57–64 (2011)

    Article  Google Scholar 

  32. Linda, O., Manic, M.: Online spatio-temporal risk assessment for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 12, 194–200 (2011)

    Article  Google Scholar 

  33. Pozna, C., Precup, R.E., Minculete, N., Antonya, C.: Characteristics of a new abstraction model. In: Proceedings of 4th International Symposium on Computational Intelligence and Intelligent Informatics (ISCIII 2009), Egypt, pp. 129–134 (2009)

    Google Scholar 

  34. Pozna, C., Precup, R.E.: Results concerning a new pattern of human knowledge. In: Proceedings of 2nd International Conference on Cognitive Infocommunications (CogInfoCom 2011), p. 18 (2011)

    Google Scholar 

  35. Horváth, L., Rudas, I.J.: Modelling and solving methods for engineers. Elsevier, Academic Press, Burlington, MA (2004)

    Google Scholar 

  36. Škrjanc, I., Blažič, S., Agamennoni, O.E.: Identification of dynamical systems with a robust interval fuzzy model. Automatica 41, 327–332 (2005)

    Article  MATH  Google Scholar 

  37. Johanyák, Z.C., Kovács, S.: Fuzzy rule interpolation based on polar cuts. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications, pp. 499–511. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  38. Kovács, L., Paláncz, B.: Glucose-Insulin Control of Type1 Diabetic Patients in H2/H ∞  Space via Computer Algebra. In: Anai, H., Horimoto, K., Kutsia, T. (eds.) AB 2007. LNCS, vol. 4545, pp. 95–109. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  39. Hulko, G., Belavy, C., Bucek, P., Ondrejkovic, K., Zajicek, P.: Engineering methods and software support for control of distributed parameter systems. In: Proceedings of 7th Asian Control Conference (ASCC 2009), Hong Kong, pp. 1432–1438 (2009)

    Google Scholar 

  40. Wu, M., Yan, J., She, J.H., Cao, W.H.: Intelligent decoupling control of gas collection process of multiple asymmetric coke ovens. IEEE Trans. Ind. Electron 56, 2782–2792 (2009)

    Article  Google Scholar 

  41. Bobal, V., Kubalcik, M., Chalupa, P., Dostal, P.: Self-tuning control of nonlinear servo system: Comparison of LQ and predictive approach. In: Proceedings of 17th Mediterranean Conference on Control and Automation (MED 2009), Thessaloniki, Greece, pp. 240–245 (2009)

    Google Scholar 

  42. Ahn, K.K., Anh, H.P.H.: Inverse double NARX fuzzy modeling for system identification. IEEE/ASME Trans. Mechatronics 15, 136–148 (2010)

    Article  Google Scholar 

  43. Sanchez Boza, A., Haber Guerra, R.: A first approach to artificial cognitive control system implementation based on the shared circuits model of sociocognitive capacities. ICIC Express Lett. 4, 1741–1746 (2010)

    Google Scholar 

  44. Abiyev, R.H., Kaynak, O.: Type 2 fuzzy neural structure for identification and control of time-varying plants. IEEE Trans. Ind. Electron 57, 4147–4159 (2010)

    Article  Google Scholar 

  45. Johanyák, Z.C.: Student evaluation based on fuzzy rule interpolation. Int. J. Artif. Intell. 5, 37–55 (2010)

    Google Scholar 

  46. Garcia, A., Luviano-Juarez, A., Chairez, I., Poznyak, A., Poznyak, T.: Projectional dynamic neural network identifier for chaotic systems: Application to Chua’s circuit. Int. J. Artif. Intell. 6, 1–18 (2011)

    Google Scholar 

  47. Baranyi, P., Lei, K.F., Yam, Y.: Complexity reduction of singleton based neuro-fuzzy algorithm. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC 2000), Nashville, TN, USA, vol. 4, pp. 2503–2508 (2000)

    Google Scholar 

  48. Baranyi, P., Tikk, D., Yam, Y., Patton, R.J.: From differential equations to PDC controller design via numerical transformation. Comp. Ind. 51, 281–297 (2003)

    Article  Google Scholar 

  49. Baranyi, P., Yam, Y., Várkonyi-Kóczy, A., Patton, R.J.: SVD based reduction to MISO TS fuzzy models. IEEE Trans. Ind. Electron 50, 232–242 (2003)

    Article  Google Scholar 

  50. Angelov, P., Buswell, R.: Identification of evolving rule-based models. IEEE Trans. Fuzzy Syst. 10, 667–677 (2002)

    Article  Google Scholar 

  51. Pedrycz, W.: Evolvable fuzzy systems: some insights and challenges. Evolving Syst. 1, 73–82 (2010)

    Article  Google Scholar 

  52. Tamani, K., Boukezzoula, R., Habchi, G.: Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems. Eng. Appl. Artif. Intell. 24, 1162–1173 (2011)

    Article  Google Scholar 

  53. Kasabov, N., Hamed, H.N.A.: Quantum-inspired particle swarm optimisation for integrated feature and parameter optimisation of evolving spiking neural networks. Int. J. Artif. Intell. 7, 114–124 (2011)

    Google Scholar 

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Pozna, C., Precup, RE. (2012). Ideas on a Pattern of Human Knowledge. In: Precup, RE., Kovács, S., Preitl, S., Petriu, E. (eds) Applied Computational Intelligence in Engineering and Information Technology. Topics in Intelligent Engineering and Informatics, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28305-5_22

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  • DOI: https://doi.org/10.1007/978-3-642-28305-5_22

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