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 ...
Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algor...
Abstract-- Computing constrained shortest paths is fundamental to some important network functions such as QoS routing, which is to find the cheapest path that satisfies certain co...
A plethora of data sources contain data entities that could be ordered according to a variety of attributes associated with the entities. Such orderings result effectively in a ra...
There are many local and greedy algorithms for energy minimization over Markov Random Field (MRF) such as iterated condition mode (ICM) and various gradient descent methods. Local ...