Non-Axiomatic Term Logic: A Computational Theory of Cognitive Symbolic Reasoning
It proposes a foundational theory for symbolic reasoning in AI, which could impact cognitive science and robotics, but it is incremental as it builds on existing logical and embedding ideas.
The paper introduces Non-Axiomatic Term Logic (NATL) as a computational framework for humanlike symbolic reasoning in AI, combining discrete syntactic elements from Aristotle's term logic with continuous semantic embeddings, but it remains theoretical without quantitative evaluation.
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.