Abstract. In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complet...
Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typ...
Constructing models of mobile agents can be difficult without domain-specific knowledge. Parametric models flexible enough to capture all mobility patterns that an expert believes...
Joshua Mason Joseph, Finale Doshi-Velez, Nicholas ...
Despite the significant progress to extend Markov Decision Processes (MDP) to cooperative multi-agent systems, developing approaches that can deal with realistic problems remains ...
Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approache...
Yan Zheng Wei, Nicholas R. Jennings, Luc Moreau, W...