Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this fa...
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
Multiagent Partially Observable Markov Decision Processes are a popular model of multiagent systems with uncertainty. Since the computational cost for finding an optimal joint pol...
— Internet routers today can be overwhelmed by a large number of BGP updates triggered by events such as session resets, link failures, and policy changes. Such excessive updates...
While Internet users claim to be concerned about online privacy, their behavior rarely reflects those concerns. In this paper we investigate whether the availability of compariso...
Julia Gideon, Lorrie Faith Cranor, Serge Egelman, ...