In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
Abstract. We propose a SAT-based algorithm for incremental diagnosis of discrete-event systems. The monotonicity is ensured by a prediction window that uses the future observations...
In this paper, we introduce a visualization method that couples a trend chart with word clouds to illustrate temporal content evolutions in a set of documents. Specifically, we us...
Event processing will play an increasingly important role in constructing enterprise applications that can immediately react to business critical events. Various technologies have...
Roger S. Barga, Jonathan Goldstein, Mohamed H. Ali...
In the context of operative disruption management, decision support systems have to evaluate the typically manifold options of responding to disturbances: The temporal shift of ac...