Modeling arbitrarily applicable relational responding with the non-axiomatic reasoning system: a Machine Psychology approach.

ABSTRACT: Arbitrarily Applicable Relational Responding (AARR) is a cornerstone of human language and reasoning, referring to the learned ability to relate symbols in flexible, context-dependent ways. In this paper, we present a novel theoretical approach for modeling AARR within an artificial intelligence framework using the Non-Axiomatic Reasoning System (NARS). NARS is an adaptive reasoning system designed for learning under uncertainty. We introduce a theoretical mechanism called acquired relations, enabling NARS to derive symbolic relational knowledge directly from sensorimotor experiences. By integrating principles from Relational Frame Theory-the behavioral psychology account of AARR-with the reasoning mechanisms of NARS, we conceptually demonstrate how key properties of AARR (mutual en

SUBMITTER: Johansson R 

PROVIDER: S-EPMC12497618 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature