Arbitrarily Applicable Same/Opposite Relational Responding with NARS

TL;DR

This paper demonstrates that NARS, a computational reasoning system, can learn and generalize arbitrary same/opposite relations from minimal training, mimicking human relational cognition.

Contribution

It extends NARS with acquired relations to enable the emergence of flexible, context-dependent relational responding, including symmetric and combinatorial entailments.

Findings

NARS rapidly internalizes trained relational rules.

NARS demonstrates derived relational generalizations.

Relational responding combines mutual and combinatorial entailments.

Abstract

Same/opposite relational responding, a fundamental aspect of human symbolic cognition, allows the flexible generalization of stimulus relationships based on minimal experience. In this study, we demonstrate the emergence of \textit{arbitrarily applicable} same/opposite relational responding within the Non-Axiomatic Reasoning System (NARS), a computational cognitive architecture designed for adaptive reasoning under uncertainty. Specifically, we extend NARS with an implementation of \textit{acquired relations}, enabling the system to explicitly derive both symmetric (mutual entailment) and novel relational combinations (combinatorial entailment) from minimal explicit training in a contextually controlled matching-to-sample (MTS) procedure. Experimental results show that NARS rapidly internalizes explicitly trained relational rules and robustly demonstrates derived relational generalizations based on arbitrary contextual cues. Importantly, derived relational responding in critical test phases inherently combines both mutual and combinatorial entailments, such as deriving same-relations from multiple explicitly trained opposite-relations. Internal confidence metrics illustrate strong internalization of these relational principles, closely paralleling phenomena observed in human relational learning experiments. Our findings underscore the potential for integrating nuanced relational learning mechanisms inspired by learning psychology into artificial general intelligence frameworks, explicitly highlighting the arbitrary and context-sensitive relational capabilities modeled within NARS.