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On Modeling Tacit Knowledge for Intelligent Systems

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Computer Science and Engineering—Theory and Applications

Abstract

In an attempt to support efforts to narrow the gap between current Artificial Intelligence and actual intelligent human behavior, this paper addresses Tacit Knowledge. Tacit Knowledge is analyzed and separated into articulable and inarticulable for ease of scrutiny. Concepts and ideas are taken up from knowledge management literature aiming to understand the scope of knowledge. Among the bailed out concepts “particulars” and “concepts” stand out, and “preconcept” is suggested as an intermediate phase between the former two. These concepts are placed into mental processes of knowledge resulting in an alternative neurological model of knowledge acquisition. The model’s target is to provide a picture as detailed as possible of the processes executed by the brain to make learning achievable. It encompasses from sensing the stimuli that is produced by the environment that are collected by sensory receptors to turn them into electrical impulses that are transmitted to the brain to climax with the emergence of concepts, from which increasingly complex knowledge is built. The model is then expanded to the social level.

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References

  1. Ertel W (2017) Introduction to artificial intelligence, 2nd ed. https://doi.org/10.1007/978-3-319-58487-4

  2. Jackson PC (1985) Introduction to artificial intelligence. Dover Publications

    Google Scholar 

  3. Akerkar R (2014) Introduction to artificial intelligence. Prentice-Hall of India, Delhi

    Google Scholar 

  4. Flasiński M (2016) Introduction to Artificial Intelligence, 1st ed. https://doi.org/10.1007/978-3-319-40022-8

  5. Bottou L (2014) From machine learning to machine reasoning. Mach Learn 94:133–149. https://doi.org/10.1007/s10994-013-5335-x

    Article  MathSciNet  Google Scholar 

  6. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260. https://doi.org/10.1126/science.aaa8415

    Article  MathSciNet  MATH  Google Scholar 

  7. Bordes A, Glorot X, Weston J, Bengio Y (2014) A semantic matching energy function for learning with multi-relational data. Mach Learn 94:233–259. https://doi.org/10.1007/s10994-013-5363-6

    Article  MathSciNet  MATH  Google Scholar 

  8. Xuan J, Lu J, Zhang G et al (2017) A Bayesian nonparametric model for multi-label learning. Mach Learn 106:1787–1815. https://doi.org/10.1007/s10994-017-5638-4

    Article  MathSciNet  MATH  Google Scholar 

  9. Weghenkel B, Fischer A, Wiskott L (2017) Graph-based predictable feature analysis. Mach Learn 106:1359–1380. https://doi.org/10.1007/s10994-017-5632-x

    Article  MathSciNet  Google Scholar 

  10. Zaidi NA, Webb GI, Carman MJ et al (2017) Efficient parameter learning of Bayesian network classifiers. Mach Learn 106:1289–1329. https://doi.org/10.1007/s10994-016-5619-z

    Article  MathSciNet  Google Scholar 

  11. Polanyi M (1959) The study of man. The University of Chicago Press, Chicago

    Google Scholar 

  12. Howells JRL (1996) Tacit knowledge, innovation and technology transfer. Technol Anal Strateg Manag 8

    Google Scholar 

  13. Stover M (2004) Making tacit knowledge explicit: the ready reference database as codified knowledge. Ref Serv Rev 32:164–173

    Article  Google Scholar 

  14. Nonaka I, Von Krogh G (2009) Perspective—Tacit knowledge and knowledge conversion: controversy and advancement in organizational knowledge creation theory. Organ Sci 20:635–652

    Article  Google Scholar 

  15. Collins H (2010) Tacit and explicit knowledge. University of Chicago Press, Chicago

    Book  Google Scholar 

  16. Polanyi M (1962) Personal knowledge: towards a post-critical philosophy. The University of Chicago Press, New York

    Google Scholar 

  17. Abbagnano N (2004) Diccionario de Filosofía. Fondo de Cultura Económica, México, D.F

    Google Scholar 

  18. Bunge MA (2007) Diccionario de filosofía. Siglo XXI: 221

    Google Scholar 

  19. Kihlstrom JF (1999) The psychological unconscious. Handb Pers Theory Res 2:424–442

    Google Scholar 

  20. Ribeiro R (2013) Tacit knowledge management. Phenomenol Cogn Sci 1–30

    Google Scholar 

  21. Machery E (2010) Precis of doing without concepts. Behav Brain Sci 33:195–206

    Article  Google Scholar 

  22. Barsalou LW (1993) Flexibility, structure, and linguistic vagary in concepts: manifestations of a compositional system of perceptual symbols. Theor Mem 1:29-31

    Google Scholar 

  23. Prinz JJ (2004) Furnishing the mind: concepts and their perceptual basis. MIT Press, Cambridge

    Google Scholar 

  24. Henshaw JM (2012) A tour of the senses: how your brain interprets the world. JHU Press, Baltimore

    Google Scholar 

  25. Ciccarelli SK, White JN (2014) Psychology, 4th edn. Pearson, London

    Google Scholar 

  26. Goldstein E (2013) Sensation and perception. Cengage Learning, Boston

    Google Scholar 

  27. Zimbardo PG, Johnson RL, Hamilton VM (2012) Psychology: core concepts, 7th edn. Pearson, London

    Google Scholar 

  28. Myers DG (2014) Exploring psychology. Worth Publishers, Basingstoke

    Google Scholar 

  29. The Society for Neuroscience (2012) Brain facts: a primer on the brain and nervous system. Soc Neurosci

    Google Scholar 

  30. Wickens A (2009) Introduction to biopsychology. Pearson Education, London

    Google Scholar 

  31. Butz M, van Ooyen A (2013) A simple rule for dendritic spine and axonal bouton formation can account for cortical reorganization after focal retinal lesions. PLoS Comput Biol 9:e1003259

    Article  Google Scholar 

  32. Dias BG, Ressler KJ (2014) Parental olfactory experience influences behavior and neural structure in subsequent generations. Nat Neurosci 17:89–96

    Article  Google Scholar 

  33. Cheetham CEJ, Barnes SJ, Albieri G et al (2014) Pansynaptic enlargement at adult cortical connections strengthened by experience. Cereb Cortex 24:521–531

    Article  Google Scholar 

  34. Barnes SJ, Finnerty GT (2010) Sensory experience and cortical rewiring. Neurosci 16:186–198

    Google Scholar 

  35. Yu H, Su Y, Shin J et al (2015) Tet3 regulates synaptic transmission and homeostatic plasticity via DNA oxidation and repair. Nat Neurosci 18:836–843

    Article  Google Scholar 

  36. Gerrans P (2005) Tacit knowledge, rule following and Pierre Bourdieu’s philosophy of social science. Anthropol Theory 5:53–74

    Article  Google Scholar 

  37. Ryle G (1949) The concept of mind. University of Chicago Press, Chicago

    Google Scholar 

  38. Wittgenstein L, Suarez AG, Moulines CU (1988) Investigaciones filosóficas. Crítica, Barcelona

    Google Scholar 

  39. Brandenberg O, Dhlamini Z, Edema R et al (2011) Module A—Introduction to molecular biology and genetic engineering. Biosafety resource book. Food and Agriculture Organization of the United Nations

    Google Scholar 

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Correspondence to Violeta Ocegueda-Miramontes .

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Ocegueda-Miramontes, V., Rodríguez-Díaz, A., Castro, J.R., Sanchez, M.A., Mendoza, O. (2018). On Modeling Tacit Knowledge for Intelligent Systems. In: Sanchez, M., Aguilar, L., Castañón-Puga, M., Rodríguez-Díaz, A. (eds) Computer Science and Engineering—Theory and Applications. Studies in Systems, Decision and Control, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-74060-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-74060-7_4

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