Probabilistic Inference Techniques for Scalable Multiagent Decision Making

In a colaboration by Singapore, USA, and Germany based researchers  Akshat Kumar, Shlomo Zilberstein, and Marc Toussaint published “Probabilistic Inference Techniques for Scalable Multiagent Decision Making“.

This paper introduces a new class of algorithms for machine learning applied to multiagent planning.  Especifically, in scenarios of partial observation.  Application of bayesian inference not being unheard of, this paper advances in determining conditions for scalability.