Abstract. We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a ...
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true ...
We describe a new approximation algorithm for solving partially observable MDPs. Our bounded policy iteration approach searches through the space of bounded-size, stochastic fini...
Performance of DNA sequencing-by-synthesis systems is fundamentally limited by the stochastic nature of the underlying biochemical process. We develop a novel graphical representa...