This paper describes the results of applying a modified version of hierarchical task-network (HTN) planning to the problem of declarer play in contract bridge. We represent inform...
Stephen J. J. Smith, Dana S. Nau, Thomas A. Throop
Most work on Predictive Representations of State (PSRs) has focused on learning and planning in unstructured domains (for example, those represented by flat POMDPs). This paper e...
David Wingate, Vishal Soni, Britton Wolfe, Satinde...
In order to assist a power plant operator to face unusual situations, we have developed an intelligent assistant that explains the suggested commands generated by an MDP-based pla...
Francisco Elizalde, Luis Enrique Sucar, Alberto Re...
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...