Active Inference for an Intelligent Agent in Autonomous Reconnaissance Missions

The paper presents an active inference route‑planning method for autonomous agents tasked with reconnaissance missions. It builds an evidence map that integrates both positive and negative sensor observations over time, using Dempster‑Shafer theory and a Gaussian sensor model to update a posterior probability distribution. By computing variational free energy across positions, the agent moves incrementally toward locations that minimize free energy, balancing exploration of large areas with exploitation of identified targets.

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