This paper contains two important contributions for the development of possibilistic causal networks. The first one concerns the representation of interventions in possibilistic ...
In this paper, we propose a policy gradient reinforcement learning algorithm to address transition-independent Dec-POMDPs. This approach aims at implicitly exploiting the locality...
: Objective: To identify factors influencing variations in clinical work in the care of patients with abdominal aortic aneurism. Method: Ethnographic observations of 26 meetings be...
This paper presents a robust and reconfigurable object tracker that integrates multiple visual features from multiple views. The tandem modular architecture stepwise refines the e...
For a given problem, the optimal Markov policy over a finite horizon is a conditional plan containing a potentially large number of branches. However, there are applications wher...