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 pose robust matching with parametric and non-parametric constraints as the problem of finding a stable independent set (SIS) in an oriented graph whose vertices are all possible...
We conduct an experimental analysis of a distributed, randomized algorithm for edge coloring graphs. The algorithm is extremely simple, yet, according to the probabilistic analysi...
Madhav V. Marathe, Alessandro Panconesi, Larry D. ...
Programslices have potentialuses in manysoftware engineering applications. Traditional slicing algorithms, however, do not work correctly on programs that contain explicit jump st...
Howard's policy iteration algorithm is one of the most widely used algorithms for finding optimal policies for controlling Markov Decision Processes (MDPs). When applied to we...