Navigating Conceptual Space; A new take on Artificial General Intelligence

TL;DR

This paper explores using neoRL for autonomous navigation in high-dimensional conceptual spaces, aiming to emulate cognition by leveraging biologically inspired learning and reasoning.

Contribution

It introduces neoRL navigation as a novel approach for emulating cognition in high-dimensional conceptual spaces, integrating neuroscience insights with reinforcement learning.

Findings

neoRL learning resembles biological learning more than traditional AI RL

neoRL navigation is adaptable across different modalities

effective in high-dimensional Euclidean spaces

Abstract

Edward C. Tolman found reinforcement learning unsatisfactory for explaining intelligence and proposed a clear distinction between learning and behavior. Tolman's ideas on latent learning and cognitive maps eventually led to what is now known as conceptual space, a geometric representation where concepts and ideas can form points or shapes.Active navigation between ideas - reasoning - can be expressed directly as purposive navigation in conceptual space. Assimilating the theory of conceptual space from modern neuroscience, we propose autonomous navigation as a valid approach for emulated cognition. However, achieving autonomous navigation in high-dimensional Euclidean spaces is not trivial in technology. In this work, we explore whether neoRL navigation is up for the task; adopting Kaelbling's concerns for efficient robot navigation, we test whether the neoRL approach is general across navigational modalities, compositional across considerations of experience, and effective when learning in multiple Euclidean dimensions. We find neoRL learning to be more resemblant of biological learning than of RL in AI, and propose neoRL navigation of conceptual space as a plausible new path toward emulated cognition.