Technology Category
Reasoning under Uncertainty: Probabilistic InferencePlanning, Routing, and Scheduling: Activity and Plan RecognitionIntelligent Robots: State Estimation
Active Inference for an Intelligent Agent in Autonomous Reconnaissance Missions
🤖 AI Summary
To address the challenges of autonomous agent path planning and collaborative situational awareness in reconnaissance, this paper proposes a dynamic path planning method grounded in active inference. The approach constructs a generative model integrating Dempster–Shafer evidence theory with a Gaussian sensor model; observation likelihoods are defined via pignistic probabilities, and decision-making is driven by variational free energy minimization. Evidence maps are updated online via Bayesian inference, enabling adaptive trade-offs between exploration and exploitation. Its key innovation lies in embedding evidence theory within the active inference framework and establishing a free energy computation paradigm linking pignistic probabilities to posterior distributions. Simulation results demonstrate significant improvements in wide-area search efficiency and target tracking robustness, while enabling multi-agent persistent co-construction and real-time updating of a unified operational situational map.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Develops active inference route-planning for autonomous reconnaissance agents
Balances exploration and exploitation in geographical area surveillance
Maintains operational picture using evidence maps and probability distributions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Active inference route-planning method for autonomous agent control
Evidence map construction using Dempster-Shafer theory and Gaussian sensor model
Variational free energy minimization guides agent movement decisions
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