UCZENIE MASZYNOWE

Uczenie maszynowe jest obszarem informatyki poświęconym metodom celowego działania komputerów bez wykorzystania algorytmów, które by z góry determinowały sposób ich działania.

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Bibliografia i literatura
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  2. Cassimatis N.L.: Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence (2007)
  3. Duch W.: Brain-inspired conscious computing architecture. Journal of Mind and Behaviour, 26:1–22 (2005)
  4. Duch W., Oentaryo R.J., Pasquier M.: Cognitive Architectures: Where Do We Go From Here? In: Artificial General Intelligence, Ed. by Pei Wang, Ben Goertzel, and Stan Franklin, IOS Press, 122–136 (2008)
  5. Duch W., Matykiewicz P., Pestian J.: Towards Understanding of Natural Language: Neurocognitive Inspirations. LNCS 4669:953–962 (2007)
  6. Goddard C.: Semantic Analysis: A Practical Introduction. Oxford University Press, 78–81 (2012)
  7. Hecht-Nielsen R.: Confabulation Theory: The Mechanism of Thought. Springer (2007)
  8. Horzyk A.: How Does Generalization and Creativity Come into Being in Neural Associative Systems and How Does It Form Human-Like Knowledge? Neurocomputing, Elsevier, DOI: 10.1016/j.neucom.2014.04.046, 238–257 (2014)
  9. Horzyk A.: Human-Like Knowledge Engineering, Generalization and Creativity in Artificial Neural Associative Systems. Springer Verlag, AISC 11156, xx–xx, (2015)
  10. Horzyk A.: How Does Human-Like Knowledge Come into Being in Artificial Associative Systems. Proc. of the 8-th International Conference on Knowledge, Information and Creativity Support Systems, Krakow, Poland, 189–200 (2013)
  11. Horzyk A.: Sztuczne systemy skojarzeniowe i asocjacyjna sztuczna inteligencja. EXIT, Warszawa, 1–276 (2013)
  12. Horzyk A.: Information Freedom and Associative Artificial Intelligence. Springer, LNAI 7267:81–89 (2012)
  13. Inverso S.A., Hawes N., Kelleher J., Allen R., and Haase K.: Think And Spell: Context-Sensitive Predictive Text for an Ambiguous Keyboard Brain-Computer Interface Speller. Journal of Biomedizinische Technik (2004)
  14. Just M.A., Varma, S.: The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, and Behavioral Neuroscience 7:153–191 (2007)
  15. Langley P., Choi D.: Learning recursive control programs from problem solving. J. of Mach. Learn. Res. 7:493–518 (2006)
  16. Nestor A., Kokinov B.: Towards Active Vision in the DUAL Cognitive Architecture. Int. J. on Information Theories and App. 11:9–15 (2004)
  17. Ogiela L., Tadeusiewicz R.: Kognitywistyka – klucz do umysłu naturalnego i sztucznego. Informatyka i psychologia w społeczeństwie informacyjnym, Wydawnictwa AGH, Kraków, 153–171 (2011)
  18. O'Reilly R.C, Munakata Y.: Computational Explorations in Cognitive Neuroscience: Understanding of the Mind by Simulating the Brain. Cambridge, MA: MIT Press (2000)
  19. Panton K., C. Matuszek, D. Lenat, D. Schneider, M. Witbrock, N. Siegel, Shepard B.: Common Sense Reasoning – From Cyc to Intelligent Assistant. In: Y. Cai and J. Abascal (Eds.): Ambient Intelligence in Everyday Life, LNAI 3864:1–31 (2006)
  20. Rutkowski, L., Metody i techniki sztucznej inteligencji, PWN, Warszawa, 2005
  21. Tadeusiewicz R., Korbicz J., Rutkowski L., Duch W. (eds.): Neural Networks in Biomedical Engineering, Biomedical Engineering – Basics and Applications, Vol. 9, Exit, Warsaw (2013)
  22. Tadeusiewicz R.: Introduction to Intelligent Systems. The Industrial Electronics Handbook - Intelligent Systems, Wilamowski B. M., Irvin J. D., Redaktorzy, Boca Raton, CRC Press, 1–12 (2011)
  23. Shapiro S.C., Rapaport W.J., Kandefer M., Johnson F.L., Goldfain A.: Metacognition in SNePS. AI Mag. 28, 17–31 (2007)
  24. Shastri L., Ajjanagadde V.: From simple associations to systematic reasoning: A connectionist encoding of rules, variables, and dynamic bindings using temporal synchrony. Behavioral && Brain Sciences 16(3), 417–494 (1993)
  25. Sowa J.F., Conceptual Structures. Reading, Mass, Addison-Wesley (1984)
  26. Sun R., Zhang X.: Top-down versus bottom-up learning in cognitive skill acquisition. Cogn. Sys. Research 5, 63–89 (2004)
  27. Wang P.: Rigid flexibility. The Logic of Intelligence, Springer (2006)