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Intensional FOL over Belnap's Billatice for Strong-AI Robotics

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arxiv 2508.02774 v1 pith:CVI677EH submitted 2025-08-04 cs.LO

Zoran Majkic This is my paper
classification cs.LO
keywords wellbelnapbillaticehumanifolintelligenceintensionallearn
verification ladder T0 review T1 audit T2 compute T3 formal
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AGI (Strong AI) aims to create intelligent robots that are quasi indistinguishable from the human mind. Like a child, the AGI robot would have to learn through input and experiences, constantly progressing and advancing its abilities over time. The AGI robot would require an intelligence more close to human's intelligence: it would have a self-aware consciousness that has the ability to solve problems, learn, and plan. Based on this approach an Intensional many-sorted First-order Logic (IFOL), as an extension of a standard FOL with Tarskian's semantics, is proposed in order to avoid the problems of standard 2-valued FOL with paradoxes (inconsistent formulae) and a necessity for robots to work with incomplete (unknown) knowledge as well. This is a more sophisticated version of IFOL with the same syntax but different semantics, able to deal with truth-ordering and knowledge-ordering as well, based on the well known Belnap's billatice with four truth-values that extend the set of classical two truth-values.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions

    cs.LO 2026-02 unverdicted novelty 4.0 of 10

    AGI robots learn and deduce using Belnap's 4-valued bilattice and Closed Knowledge Assumption to expand knowledge while supporting inconsistencies and providing logical security.

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

    cs.AI 2026-07 reject novelty 3.0 of 10

    A theoretical paper defines global and local 'symmetry transformations' to assign probabilities to unknown sentences in a Belnap four-valued intensional logic, with no implementation or validation.

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