What if planning is not about computing value functions but about performing probabilistic inference? Marc Toussaint shows how recasting optimal control as message passing opens new computational pathways for robotics and decision-making.

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Marc Toussaint presents a theoretical framework that reformulates planning and optimal control as probabilistic inference in graphical models. Rather than iterating backward through Bellman equations to compute value functions, his approach computes both forward and backward messages whose product yields a posterior distribution over actions. This shift in perspective is not merely notational: it leads to genuinely different approximation algorithms, particularly for complex problems like partially observable Markov decision proce ...