Active Inference and Epistemic Value in Graphical Models

Frontiers in Robotics and AI

Bart van Erp¹  Thijs van de Laar¹  Bert de Vries²  Magnus Koudahl³ 

[1] Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands;GN Hearing Benelux BV, Eindhoven, Netherlands;Nested Minds Network Ltd., Liverpool, United Kingdom;

关键词: free energy principle;    active inference;    variational optimization;    constrained bethe free energy;    message passing;   

DOI  :  10.3389/frobt.2022.794464

来源: DOAJ

【 摘 要 】

The Free Energy Principle (FEP) postulates that biological agents perceive and interact with their environment in order to minimize a Variational Free Energy (VFE) with respect to a generative model of their environment. The inference of a policy (future control sequence) according to the FEP is known as Active Inference (AIF). The AIF literature describes multiple VFE objectives for policy planning that lead to epistemic (information-seeking) behavior. However, most objectives have limited modeling flexibility. This paper approaches epistemic behavior from a constrained Bethe Free Energy (CBFE) perspective. Crucially, variational optimization of the CBFE can be expressed in terms of message passing on free-form generative models. The key intuition behind the CBFE is that we impose a point-mass constraint on predicted outcomes, which explicitly encodes the assumption that the agent will make observations in the future. We interpret the CBFE objective in terms of its constituent behavioral drives. We then illustrate resulting behavior of the CBFE by planning and interacting with a simulated T-maze environment. Simulations for the T-maze task illustrate how the CBFE agent exhibits an epistemic drive, and actively plans ahead to account for the impact of predicted outcomes. Compared to an EFE agent, the CBFE agent incurs expected reward in significantly more environmental scenarios. We conclude that CBFE optimization by message passing suggests a general mechanism for epistemic-aware AIF in free-form generative models.

【 授权许可】

Unknown