Identifying and inferring performances of a network topology is a well known problem. Achieving this by using only end-to-end measurements at the application level is a method kno...
In this paper, we focus on the problem of learning reactive skills for use by physical agents. We propose a new representation for such procedures, teleoreactive logic programs, al...
Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of comp...
We propose a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of...
For simulations involving complex objects, a number of different properties must be represented. An example of this is in modeling an object undergoing combustion—heat amounts,...