We present a general approach to combine symbolic state space representations for the discrete and continuous parts in the synthesis of winning strategies for timed reachability ga...
We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects....
This paper presents a framework for multiresolution compression and geometric reconstruction of arbitrarily dimensioned data designed for distributed applications. Although being ...
We describe an innovative solution to the problem of scheduling astronomy observations for the Stratospheric Observatory for Infrared Astronomy, an airborne observatory. The probl...
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...