Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
More and more effort is made to provide methodologies for the development of agent–based systems. Awareness has grown that these are necessary to develop high quality agent syst...
Jan Sudeikat, Lars Braubach, Alexander Pokahr, Win...
This volume is intended to help advance the field of artificial neural networks along the lines of complexity present in animal brains. In particular, we are interested in examin...
Our world is increasingly data-driven. The growth and value of data continue to exceed all predictions. Potential for business opportunity, economic growth, scientific discovery, ...
Health care officials are increasingly concerned with knowing early whether an outbreak of a particular disease is unfolding. We often have daily counts of some variable that are ...