Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL

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[Submitted on 12 Jul 2026]

Title:Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL

View a PDF of the paper titled Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL, by Zoran Majkic

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Abstract:Neuro-symbolic AI based on $IFOL_B$ is a way to combine neural learning and symbolic reasoning to overcome limitations of purely neural systems (like lack of interpretability and logical structure) with formal logical machinery for self-reference. In this paper we expand the cognitive power of $IFOL_B$ by using the probability computation for the currently unknown sentences, based on Nilsson's probability structure for the $IFOL_B$. We introduce the global symmetry transformation that preserves the current knowledge database and logical deduction, and the local one used for real-time decisions about concrete (sub)problems that involve only a very strict subset of $IFOL_B$ predicates. The computation of probability density function $KI$ in both cases, based on the Shannon's maximum information entropy, is provided by neural networks of this probabilistic neuro-symbolic AGI.

Comments: 32 pages

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.13073 [cs.AI]

(or arXiv:2607.13073v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.13073

arXiv-issued DOI via DataCite

Submission history

From: Zoran Majkic [view email] [v1] Sun, 12 Jul 2026 15:54:58 UTC (49 KB)

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