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

TL;DR

This paper proposes an advanced Intensional many-sorted First-order Logic (IFOL) based on Belnap's bilattice to enhance reasoning in Strong AI robotics, addressing issues of inconsistency and incomplete knowledge.

Contribution

It introduces a novel version of IFOL with Belnap's bilattice semantics, enabling better handling of paradoxes and incomplete information in AGI systems.

Findings

Enhanced logical framework for AGI reasoning

Ability to manage inconsistent and incomplete knowledge

Improved robustness over standard FOL in AI applications

Abstract

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.