Active Inference for an Intelligent Agent in Autonomous Reconnaissance Missions

TL;DR

This paper introduces an active inference-based route-planning method for autonomous agents in reconnaissance missions, using evidence maps and Bayesian updates to optimize exploration and target tracking.

Contribution

It presents a novel active inference framework combining Dempster-Shafer theory and Bayesian methods for autonomous route planning in reconnaissance tasks.

Findings

Effective balance between exploration and exploitation achieved.

Simulation results demonstrate improved target tracking.

Evidence map approach enhances situational awareness.

Abstract

We develop an active inference route-planning method for the autonomous control of intelligent agents. The aim is to reconnoiter a geographical area to maintain a common operational picture. To achieve this, we construct an evidence map that reflects our current understanding of the situation, incorporating both positive and "negative" sensor observations of possible target objects collected over time, and diffusing the evidence across the map as time progresses. The generative model of active inference uses Dempster-Shafer theory and a Gaussian sensor model, which provides input to the agent. The generative process employs a Bayesian approach to update a posterior probability distribution. We calculate the variational free energy for all positions within the area by assessing the divergence between a pignistic probability distribution of the evidence map and a posterior probability distribution of a target object based on the observations, including the level of surprise associated with receiving new observations. Using the free energy, we direct the agents' movements in a simulation by taking an incremental step toward a position that minimizes the free energy. This approach addresses the challenge of exploration and exploitation, allowing agents to balance searching extensive areas of the geographical map while tracking identified target objects.