Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL
Quick Answer
This paper enhances neuro-symbolic AI using Belnap's Typed Intensional First-Order Logic ($IFOL_B$) by integrating probabilistic computations for unknown sentences.
Quick Take
It introduces a global symmetry transformation for knowledge preservation and a local transformation for real-time decision-making, leveraging neural networks to compute probability density functions based on Shannon's maximum information entropy.
Key Points
- Expands cognitive capabilities of $IFOL_B$ with probabilistic reasoning.
- Introduces global and local symmetry transformations for knowledge and decision-making.
- Utilizes neural networks to compute probability density functions.
- Addresses limitations of purely neural systems like interpretability.
- Enhances real-time decision-making for specific subproblems.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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