Abstract—Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works...
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
Decentralized partially observable MDPs (DEC-POMDPs) provide a rich framework for modeling decision making by a team of agents. Despite rapid progress in this area, the limited sc...
Abstract— A Model Predictive Control (MPC) -based approach is presented for autonomous path following via Active Front Steering (AFS). We start from the Nonlinear MPC (NMPC) prob...
Giovanni Palmieri, Paolo Falcone, H. Eric Tseng, L...
—For a large-scale mesh network with dynamic traffic, maintaining the global state information in a centralized fashion is impractical. Hence, distributed schemes are needed to ...