Per R. Leikanger

Per R. Leikanger

The neoRL framework for purposive AI implements latent learning by emulated cognitive maps, with general value functions (GVF) expressing operant desires toward separate states. The agent's expectancy of reward, expressed as learned projections in the considered space, allows the neoRL agent to extract purposive behavior from the learned map according to the reward hypothesis. We explore this allegory further, considering neoRL modules as nodes in a network with desire as input and state-action Q-value as output; we see that action sets with Euclidean significance imply an interpretation of state-action vectors as Euclidean projections of desire. Autonomous desire from neoRL nodes within the agent allows for deeper neoRL behavioral graphs. Experiments confirm the effect of neoRL networks governed by autonomous desire, verifying the four principles for purposive networks. A neoRL agent governed by purposive networks can navigate Euclidean spaces in real-time while learning, exemplifying how modern AI still can profit from inspiration from early psychology.

Per R. Leikanger

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.

Per R. Leikanger

Navigating the world is a fundamental ability for any living entity. Accomplishing the same degree of freedom in technology has proven to be difficult. The brain is the only known mechanism capable of voluntary navigation, making neuroscience our best source of inspiration toward autonomy. Assuming that state representation is key, we explore the difference in how the brain and the machine represent the navigational state. Where Reinforcement Learning (RL) requires a monolithic state representation in accordance with the Markov property, Neural Representation of Euclidean Space (NRES) reflects navigational state via distributed activation patterns. We show how NRES-Oriented RL (neoRL) agents are possible before verifying our theoretical findings by experiments. Ultimately, neoRL agents are capable of behavior synthesis across state spaces -- allowing for decomposition of the problem into smaller spaces, alleviating the curse of dimensionality.