A new learning algorithmis derived which performs online stochastic gradient ascent in the mutual informationbetween outputs and inputs of a network. In the absence of a priori kn...
This work is a suitability study of the different optimization methods for automated parameter estimation (fitting) in the context of neuronal signaling networks. The Gepasi simul...
We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately...
Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos A. ...
We study an inventory system under periodic review in the presence of two suppliers (or delivery modes). The emergency supplier has a shorter lead-time than the regular supplier, ...
Stochastic games are a generalization of MDPs to multiple agents, and can be used as a framework for investigating multiagent learning. Hu and Wellman (1998) recently proposed a m...