We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomiz...
This paper studies evolutionary clustering, which is a recently hot topic with many important applications, noticeably in social network analysis. In this paper, based on the rece...
Tianbing Xu, Zhongfei (Mark) Zhang, Philip S. Yu, ...
Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from da...
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...