RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
We address the problem of similarity search in large time series databases. We introduce a novel indexing algorithm that allows faster retrieval. The index is formed by creating b...
Path profiles record the frequencies of execution paths through a program. Until now, the best global instruction schedulers have relied upon profile-gathered frequencies of condi...
We provide a method whereby, given mode and (upper approximation) type information, we can detect procedures and goals that can be guaranteed to not fail (i.e., to produce at leas...
The problem of maintaining an approximate solution for one-dimensional bin packing when items may arrive and depart dynamically is studied. In accordance with various work on fully...