Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
The usefulness of parameterized algorithmics has often depended on what Niedermeier has called, "the art of problem parameterization." In this paper we introduce and expl...
Michael R. Fellows, Serge Gaspers, Frances A. Rosa...
We investigate the extent to which price updates can increase the revenue of a seller with little prior information on demand. We study prior-free revenue maximization for a selle...
In this paper, we describe the TIMBER XML database system implemented at University of Michigan. TIMBER was one of the first native XML database systems, designed from the ground ...
Background: Automated software tools for multiple alignment often fail to produce biologically meaningful results. In such situations, expert knowledge can help to improve the qua...
Burkhard Morgenstern, Sonja J. Prohaska, Dirk P&ou...