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Guiding Probabilistic Logical Inference with Nonlinear Dynamical Attention Allocation

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8598)

Abstract

In order to explore the practical manifestations of the “cognitive synergy” between the PLN (Probabilistic Logic Networks) and ECAN (Economic Attention Network) components of the OpenCog AGI architecture, we explore the behavior of PLN and ECAN operating together on two standard test problems commonly used with Markov Logic Networks (MLN). Our preliminary results suggest that, while PLN can address these problems adequately, ECAN offers little added value for the problems in their standard form. However, we outline modified versions of the problem that we hypothesize would demonstrate the value of ECAN more effectively, via inclusion of confounding information that needs to be heuristically sifted through.

Keywords

  • Test Problem
  • Logic Network
  • General Intelligence
  • Markov Logic Network
  • VLDB Endowment

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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© 2014 Springer International Publishing Switzerland

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Harrigan, C., Goertzel, B., Iklé, M., Belayneh, A., Yu, G. (2014). Guiding Probabilistic Logical Inference with Nonlinear Dynamical Attention Allocation. In: Goertzel, B., Orseau, L., Snaider, J. (eds) Artificial General Intelligence. AGI 2014. Lecture Notes in Computer Science(), vol 8598. Springer, Cham. https://doi.org/10.1007/978-3-319-09274-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-09274-4_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09273-7

  • Online ISBN: 978-3-319-09274-4

  • eBook Packages: Computer ScienceComputer Science (R0)