Non-Axiomatic Term Logic: A Computational Theory of Cognitive Symbolic Reasoning

TL;DR

Non-Axiomatic Term Logic (NATL) is a theoretical framework combining Aristotle's term logic with modern distributed representations to model human-like symbolic reasoning in AI.

Contribution

The paper introduces NATL as a novel hybrid logical-semantic framework inspired by classical logic and modern embeddings, advancing the theoretical understanding of cognitive reasoning.

Findings

Qualitative analysis of arguments using NATL

Discussion of applications in cognitive science and robotics

Identification of challenges for implementation

Abstract

This paper presents Non-Axiomatic Term Logic (NATL) as a theoretical computational framework of humanlike symbolic reasoning in artificial intelligence. NATL unites a discrete syntactic system inspired from Aristotle's term logic and a continuous semantic system based on the modern idea of distributed representations, or embeddings. This paper positions the proposed approach in the phylogeny and the literature of logic, and explains the framework. As it is yet no more than a theory and it requires much further elaboration to implement it, no quantitative evaluation is presented. Instead, qualitative analyses of arguments using NATL, some applications to possible cognitive science/robotics-related research, and remaining issues towards a machinery implementation are discussed.