We present a novel linear clustering framework (DIFFRAC) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem. The...
We consider the problem of planning optimally in potentially concurrent probabilistic domains: actions have probabilistic effects and may execute in parallel under certain conditi...
We present an efficient framework for dynamic reconfiguration of application-specific custom instructions. A key component of this framework is an iterative algorithm for temporal...
We consider the problem of planning optimally in potentially concurrent probabilistic domains: actions have probabilistic effects and may execute in parallel under certain conditi...
The reuse of well-tested and optimized design objects is an important aspect for decreasing design times, increasing design quality, and improving the predictability of designs. R...