Stochastization of weighted automata

Nondeterministic weighted finite automata (WFAs) map input words to real numbers. Each transition of a WFA is labeled by both a letter from some alphabet and a weight. The weight of a run is the sum of the weights on the transitions it traverses, and the weight of a word is the minimal weight of a run on it. In probabilistic weighted automata (PWFAs), the transitions are further labeled by probabilities, and the weight of a word is the expected weight of a run on it. We define and study stochastization of WFAs: given a WFA A, stochastiza-tion turns it into a PWFA A′ by labeling its transitions by probabilities. The weight of a word in A′ can only increase with respect to its weight in A, and we seek stochastizations in which A′ α-approximates A for the minimal possible factor a α; 1. That is, the weight of every word in A′ is at most a times its weight in A. We show that stochastization is useful in reasoning about the competitive ratio of randomized online algorithms and in approximated determinization of WFAs. We study the problem of deciding, given a WFA A and a factor α ≥ 1, whether there is a stochastization of A that achieves an α-approximation. We show that the problem is in general undecidable, yet can be solved in PSPACE for a useful class of WFAs.