We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...
We explore a means to both model and reason about partial observability within the scope of constraintbased temporal reasoning. Prior studies of uncertainty in Temporal CSPs have ...
It is not unusual that an approximate model is needed for fitness evaluation in evolutionary computation. In this case, the convergence properties of the evolutionary algorithm are...
We introduce a theoretical framework for discovering relationships between two database instances over distinct and unknown schemata. This framework is grounded in the context of ...
Abstract— Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this cont...