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Partial Operator Induction with Beta Distributions

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Book cover Artificial General Intelligence (AGI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10999))

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Abstract

A specialization of Solomonoff Operator Induction considering partial operators described by second order probability distributions, and more specifically Beta distributions, is introduced. An estimate to predict the second order probability of new data, obtained by averaging the second order distributions of partial operators, is derived. The problem of managing the partiality of the operators is presented. A simplistic solution based on estimating the Kolmogorov complexity of perfect completions of partial operators is given.

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Notes

  1. 1.

    More by necessity, since the set of partial operators is enumerable, while the set of complete ones is not.

References

  1. Abourizk, S., Halpin, D., Wilson, J.: Fitting beta distributions based on sample data. J. Constr. Eng. Manag. 120, 288–305 (1994)

    Article  Google Scholar 

  2. Goertzel, B.: Toward a formal characterization of real-world general intelligence. In: Proceedings of 3rd International Conference on Artificial General Intelligence (2010)

    Google Scholar 

  3. Goertzel, B.: Probabilistic growth and mining of combinations: a unifying meta-algorithm for practical general intelligence. In: Steunebrink, B., Wang, P., Goertzel, B. (eds.) AGI -2016. LNCS (LNAI), vol. 9782, pp. 344–353. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41649-6_35

    Chapter  Google Scholar 

  4. Goertzel, B., et al.: Speculative scientific inference via synergetic combination of probabilistic logic and evolutionary pattern recognition. In: Bieger, J., Goertzel, B., Potapov, A. (eds.) AGI 2015. LNCS (LNAI), vol. 9205, pp. 80–89. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21365-1_9

    Chapter  Google Scholar 

  5. Goertzel, B., Ikle, M., Goertzel, I.F., Heljakka, A.: Probabilistic Logic Networks. Springer, US (2009)

    Book  Google Scholar 

  6. Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: a tutorial. Statist. Sci. 14(4), 382–417 (1999)

    Article  MathSciNet  Google Scholar 

  7. Hutter, M.: Optimality of universal Bayesian sequence prediction for general loss and alphabet. J. Mach. Learn. Res. 4, 971–1000 (2003)

    MathSciNet  MATH  Google Scholar 

  8. Ikle, M., Goertzel, B.: Probabilistic quantifier logic for general intelligence: an indefinite probabilities approach. In: First International Conference on Artificial General Intelligence, pp. 188–199 (2008)

    Google Scholar 

  9. Solomonoff, R.J.: Three kinds of probabilistic induction: universal distributions and convergence theorems. Comput. J. 51, 566–570 (2008)

    Article  Google Scholar 

  10. Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proc. Royal Soc. London Ser. A 186, 453–461 (1946)

    Article  MathSciNet  Google Scholar 

  11. Li, M., Vitanyi, P.: An Introduction to Kolmogorov Complexity and Its Applications. Springer, New York (1997). https://doi.org/10.1007/978-1-4757-2606-0

    Book  MATH  Google Scholar 

  12. Schafer, J.L., Graham, J.W.: Missing data: our view of the state of the art. Psychol. Methods 7, 147–177 (2002)

    Article  Google Scholar 

  13. Weisstein, E.W.: Regularized beta function. From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/RegularizedBetaFunction.html. Accessed 20 Apr 2018

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Author information

Authors and Affiliations

  1. SingularityNET Foundation, Amsterdam, The Netherlands

    Nil Geisweiller

  2. OpenCog Foundation, Wilmington, DE, USA

    Nil Geisweiller

  3. Novamente LLC, Rockville, MD, USA

    Nil Geisweiller

Authors
  1. Nil Geisweiller
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Corresponding author

Correspondence to Nil Geisweiller .

Editor information

Editors and Affiliations

  1. Adams State University, Alamosa, Colorado, USA

    Matthew Iklé

  2. Odessa Competence Center for Artificial Intelligence (OCCAM), Odessa, Ukraine

    Arthur Franz

  3. Hokkaido University, Sapporo, Japan

    Rafal Rzepka

  4. SingularityNET; OpenCog; Hanson Robotics, Hong Kong, China

    Ben Goertzel

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Geisweiller, N. (2018). Partial Operator Induction with Beta Distributions. In: Iklé, M., Franz, A., Rzepka, R., Goertzel, B. (eds) Artificial General Intelligence. AGI 2018. Lecture Notes in Computer Science(), vol 10999. Springer, Cham. https://doi.org/10.1007/978-3-319-97676-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-97676-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97675-4

  • Online ISBN: 978-3-319-97676-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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