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

2026/07/12 by Zoran Majkic
#cs.AI

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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.

Citations

• Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions
• Deep Neural Networks are Easily Fooled: High Confidence Predictions for\n Unrecognizable Images
• SOAR: An architecture for general intelligence

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