Cooperative multi-agent inference over grid structured Markov random fields

In this work we investigate cooperative inference in multi-agent systems where uncertainty is modeled by the grid structured pairwise Markov random field. A framework is proposed, which we term the multi-agent Markov random field, that decomposes the global inference problem into inter-agent belief exchanges over a hypertree topology and local intra-agent inference problems. Due to the exponential complexity of exact inference, we propose a loopy belief propagation algorithm for approximate inference over appropriately formed local generalized cluster graphs. Both synchronous and intelligent message passing are considered and a grid scale-invariant scheme based on the notion of regions of influence in a cluster graph is presented. The algorithms are simulated over a grid workspace with a team of virtual Autonomous Surface Vehicles (ASVs), with the goal of spatial plume detection in oceanographic data captured from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. We show that while the exact method produces predictably accurate and smooth grid maps, the approximate method competes well in terms of plume detection rate with the region of influence message passing scheme excelling over large tasks due to a lack of dependence on grid size.