This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these app...
Progressive processing plans allow systems to tradeoff computational resources against the quality of service by specifying alternative ways in which to accomplish each step. When ...
Shlomo Zilberstein, Abdel-Illah Mouaddib, Andrew A...
In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underli...
Discrete-time optimal control problems arise naturally in many economic problems. Despite the rapid growth in computing power and new developments in the literature, many economic...
We are using ML to build a compiler that does low-level optimization. To support optimizations in classic imperative style, we built a control-flow graph using mutable pointers an...