Synthetic Spatial Foraging With Active Inference in a Geocaching Task

Abstract

Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent’s actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating—that underwrites spatial foraging—and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.

Structure learning enhances concept formation in synthetic Active Inference agents

“…In this work, agents possess and update an internal model that entertains temporally and physically structured processes when interpreting these orderly interrelationships. Whereas processes such as inference and parametric learning have been discussed extensively in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], little attention has been given to the type of off-line learning we define operationally as structure learning [18,19], with the implicit computational form within the Active Inference framework. This is a nascent field of inquiry raising important questions about what it means to process information off-line.…”