Domain knowledge is essential for successful problem solving and optimization. This paper introduces a framework in which a form of automatic domain knowledge extraction can be im...
The linear systems associated with large, sparse, symmetric, positive definite matrices are often solved iteratively using the preconditioned conjugate gradient method. We have d...
We present an algorithm that quickly finds optimal plans for unforeseen agent preferences within graph-based planning domains where actions have deterministic outcomes and action ...
This paper proposes a framework that provides significant speed-ups and also improves the effectiveness of general message passing algorithms based on dual LP relaxations. It is ap...
Stochastic simulations and other scientific applications that depend on random numbers are increasingly implemented in a parallelized manner in programmable logic. High-quality ps...