The planning as heuristic search framework, initiated by the planners ASP from Bonet, Loerincs and Geffner, and HSP from Bonet and Geffner, lead to some of the most performant pla...
The existing reinforcement learning approaches have been suffering from the curse of dimension problem when they are applied to multiagent dynamic environments. One of the typical...
Determining the evolutionary history of a given biological data is an important task in biological sciences. Given a set of quartet topologies over a set of taxa, the Maximum Quart...
Currently, Graphplan and Blackbox, which converts Graphplan's plan graph into the satisfaction (SAT) problem, are two of the most successful planners. Since Graphplan gains i...
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...