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 ...
The construction of multicast trees is complicated by the need to balance a number of important objectives, including: minimizing latencies, minimizing depth/hops, and bounding th...
We investigate the average-case speed and scalability of parallel algorithms executing on multiprocessors. Our performance metrics are average-speed and isospeed scalability. By m...
An instance of the path hitting problem consists of two families of paths, D and H, in a common undirected graph, where each path in H is associated with a non-negative cost. We r...
Many large-scale networks such as ad hoc and sensor networks, peer-to-peer networks, or the Internet have the property that the number of independent nodes does not grow arbitrari...