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Probabilistic Logic Networks for Temporal and Procedural Reasoning

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Artificial General Intelligence (AGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13921))

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Abstract

Probabilistic Logic Networks (PLN) offers an excellent theory to frame learning and planning as a form of reasoning. This paper offers a complement to the seminal PLN book [3], in particular to its Chapter 14 on temporal and procedural reasoning, by providing formal definitions of temporal constructs, as well as inference rules necessary to carry temporal and procedural reasoning.

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Notes

  1. 1.

    because links can point to links, not just nodes.

  2. 2.

    To be precise, \(\mathcal {P}r(Q|P)\) should be \(\mathcal {P}r(\mathcal {S}at(Q)|\mathcal {S}at(P))\), where \(\mathcal {S}at(P)\) and \(\mathcal {S}at(Q)\) are the satisfying sets of P and Q respectively.

  3. 3.

    A perfectly reliable piece of evidence has a confidence of 1. Dealing with unreliable evidence involves using convolution products and is outside of the scope of this paper.

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Authors and Affiliations

  1. SingularityNET Foundation, Amsterdam, The Netherlands

    Nil Geisweiller & Hedra Yusuf

Authors
  1. Nil Geisweiller
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  2. Hedra Yusuf
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Corresponding authors

Correspondence to Nil Geisweiller or Hedra Yusuf .

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Editors and Affiliations

  1. Department of Psychology, Stockholm University, Stockholm, Sweden

    Patrick Hammer

  2. Örebro University, Örebro, Sweden

    Marjan Alirezaie

  3. University of Gothenburg, Gothenburg, Sweden

    Claes Strannegård

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Geisweiller, N., Yusuf, H. (2023). Probabilistic Logic Networks for Temporal and Procedural Reasoning. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_9

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  • DOI: https://doi.org/10.1007/978-3-031-33469-6_9

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