Abstract. Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled...
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
Empirical filter designs generalize relationships inferred from training data to effect realistic solutions that conform well to the human visual system. Complex algorithms invol...
In standard predictive video coders, intra-mode coding of macroblocks (MBs) provides packet loss resilience, at the cost of reduced compression efficiency. Conventional mode selec...
In this paper, we utilize a predator-prey model in order to identify characteristics of single-objective variation operators in the multi-objective problem domain. In detail, we a...
Christian Grimme, Joachim Lepping, Alexander Papas...