Title: Investigating Enactive Learning for Autonomous Intelligent Agents,
Abstract: The enactive approach to cognition is typically proposed as a viable
alternative to traditional cognitive science. Enactive cognition displaces the
explanatory focus from the internal representations of the agent to the direct
sensorimotor interaction with its environment. In this paper, we investigate
enactive learning through means of artificial agent simulations. We compare the
performances of the enactive agent to an agent operating on classical
reinforcement learning in foraging tasks within maze environments. The
characteristics of the agents are analysed in terms of the accessibility of the
environmental states, goals, and exploration/exploitation tradeoffs. We confirm
that the enactive agent can successfully interact with its environment and
learn to avoid unfavourable interactions using intrinsically defined goals. The
performance of the enactive agent is shown to be limited by the number of
affordable actions.