We present a new probabilistic framework for finding likely variable assignments in difficult constraint satisfaction problems. Finding such assignments is key to efficient sea...
Eric I. Hsu, Matthew Kitching, Fahiem Bacchus, She...
We describe an application of machine learning to the problem of geomorphic mapping of planetary surfaces. Mapping landforms on planetary surfaces is an important task and the fi...
Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta
We consider the problem of grasping novel objects in cluttered environments. If a full 3-d model of the scene were available, one could use the model to estimate the stability and...
Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors...
Planning under uncertainty involves two distinct sources of uncertainty: uncertainty about the effects of actions and uncertainty about the current state of the world. The most wi...