Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
This paper explains some analyses that can be performed on a hierarchical finite state machine to validate that it performs as intended. Such a hierarchical state machine has tra...
Enterprise Architecture (EA) requires modeling enterprises across multiple levels (from markets down to IT systems). Providing tool support for such models is a challenge (e.g. mo...
Diffuse Optical Tomography (DOT) poses a typical illposed inverse problem with limited number of measurements and inherently low spatial resolution. In this paper, we propose a hi...
Murat Guven, Birsen Yazici, Xavier Intes, Britton ...
Scientific workflows have recently emerged as a new paradigm for representing and managing complex distributed scientific computations and data analysis, and have enabled and acce...