Quantitative games are two-player zero-sum games played on directed weighted graphs. Total-payoff games-that can be seen as a refinement of the well-studied mean-payoff games-are the variant where the payoff of a play is computed as the sum of the weights. Our aim is to describe the first pseudo-polynomial time algorithm for total-payoff games in the presence of arbitrary weights. It consists of a non-trivial application of the value iteration paradigm. Indeed, it requires to study, as a milestone, a refinement of these games, called min-cost reachability games, where we add a reachability objective to one of the players. For these games, we give an efficient value iteration algorithm to compute the values and optimal strategies (when they exist), that runs in pseudo-polynomial time. We also propose heuristics to speed up the computations.
We study finite automata running over infinite binary trees. A run of such an automaton over an input tree is a tree labeled by control states of the automaton: the labeling is built in a top-down fashion and should be consistent with the transitions of the automaton. A branch in a run is accepting if the ω-word obtained by reading the states along the branch satisfies some acceptance condition (typically an ω-regular condition such as a Büchi or a parity condition). Finally, a tree is accepted by the automaton if there exists a run over this tree in which every branch is accepting.In this article, we consider two relaxations of this definition, introducing a qualitative aspect. First, we relax the notion of accepting run by allowing a negligible set (in the sense of measure theory) of nonaccepting branches. In this qualitative setting, a tree is accepted by the automaton if there exists a run over this tree in which almost every branch is accepting. This leads to a new class of tree languages, qualitative tree languages. This class enjoys many good properties: closure under union and intersection (but not under complement), and emptiness is decidable in polynomial time. A dual class, positive tree languages, is defined by requiring that an accepting run contains a non-negligeable set of branches.The second relaxation is to replace the existential quantification (a tree is accepted if there exists some accepting run over the input tree) with a probabilistic quantification (a tree is accepted if almost every run over the input tree is accepting). For the run, we may use either classical acceptance or qualitative acceptance. In particular, for the latter, we exhibit a tight connection with partial observation Markov decision processes. Moreover, if we additionally restrict operation to the Büchi condition, we show that it leads to a class of probabilistic automata on infinite trees enjoying a decidable emptiness problem. To our knowledge, this is the first positive result for a class of probabilistic automaton over infinite trees.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.