Decentralized partially observable Markov decision processes (DEC-POMDPs) form a general framework for planning for groups of cooperating agents that inhabit a stochastic and part...
Matthijs T. J. Spaan, Geoffrey J. Gordon, Nikos A....
A wide variety of function approximation schemes have been applied to reinforcement learning. However, Bayesian filtering approaches, which have been shown efficient in other field...
In this paper, we present a variational Bayesian (VB) approach to computing the interval estimates for nonhomogeneous Poisson process (NHPP) software reliability models. This appr...
Hiroyuki Okamura, Michael Grottke, Tadashi Dohi, K...
— It has been known since the early 1990s that backpressure-type algorithms for communication networks (such as the Max-Weight algorithm) can be used to approximately solve stati...
Abstract— This paper studies routing schemes and their distributed construction in limited wireless networks, such as sensor or mesh networks. We argue that the connectivity of s...