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 entailment, combinatorial entailment, and transformation of stimulus functions) can emerge from NARS's inference rules and memory structures. Two theoretical demonstrations illustrate this approach: one modeling stimulus equivalence and transfer of function, and another modeling complex relational networks involving opposition frames. In both cases, the system logically demonstrates the derivation of untrained relations and context-sensitive transformations of stimulus functions, mirroring established human cognitive phenomena. These results suggest that AARR-long considered uniquely human-can be conceptually captured by suitably designed AI systems, emphasizing the value of integrating behavioral science insights into artificial general intelligence (AGI) research. Empirical validation of this theoretical approach remains an essential future direction.