19 research outputs found
Weighted Automata and Logics for Infinite Nested Words
Nested words introduced by Alur and Madhusudan are used to capture structures
with both linear and hierarchical order, e.g. XML documents, without losing
valuable closure properties. Furthermore, Alur and Madhusudan introduced
automata and equivalent logics for both finite and infinite nested words, thus
extending B\"uchi's theorem to nested words. Recently, average and discounted
computations of weights in quantitative systems found much interest. Here, we
will introduce and investigate weighted automata models and weighted MSO logics
for infinite nested words. As weight structures we consider valuation monoids
which incorporate average and discounted computations of weights as well as the
classical semirings. We show that under suitable assumptions, two resp. three
fragments of our weighted logics can be transformed into each other. Moreover,
we show that the logic fragments have the same expressive power as weighted
nested word automata.Comment: LATA 2014, 12 page
Weighted Operator Precedence Languages
In the last years renewed investigation of operator precedence languages (OPL) led to discover important properties thereof: OPL are closed with respect to all major operations, are characterized, besides the original grammar family, in terms of an automata family (OPA) and an MSO logic; furthermore they significantly generalize the well-known visibly pushdown languages (VPL). In another area of research, quantitative models of systems are also greatly in demand. In this paper, we lay the foundation to marry these two research fields. We introduce weighted operator precedence automata and show how they are both strict extensions of OPA and weighted visibly pushdown automata. We prove a Nivat-like result which shows that quantitative OPL can be described by unweighted OPA and very particular weighted OPA. In a Büchi-like theorem, we show that weighted OPA are expressively equivalent to a weighted MSO-logic for OPL
Qualitative Analysis of Concurrent Mean-payoff Games
We consider concurrent games played by two-players on a finite-state graph,
where in every round the players simultaneously choose a move, and the current
state along with the joint moves determine the successor state. We study a
fundamental objective, namely, mean-payoff objective, where a reward is
associated to each transition, and the goal of player 1 is to maximize the
long-run average of the rewards, and the objective of player 2 is strictly the
opposite. The path constraint for player 1 could be qualitative, i.e., the
mean-payoff is the maximal reward, or arbitrarily close to it; or quantitative,
i.e., a given threshold between the minimal and maximal reward. We consider the
computation of the almost-sure (resp. positive) winning sets, where player 1
can ensure that the path constraint is satisfied with probability 1 (resp.
positive probability). Our main results for qualitative path constraints are as
follows: (1) we establish qualitative determinacy results that show that for
every state either player 1 has a strategy to ensure almost-sure (resp.
positive) winning against all player-2 strategies, or player 2 has a spoiling
strategy to falsify almost-sure (resp. positive) winning against all player-1
strategies; (2) we present optimal strategy complexity results that precisely
characterize the classes of strategies required for almost-sure and positive
winning for both players; and (3) we present quadratic time algorithms to
compute the almost-sure and the positive winning sets, matching the best known
bound of algorithms for much simpler problems (such as reachability
objectives). For quantitative constraints we show that a polynomial time
solution for the almost-sure or the positive winning set would imply a solution
to a long-standing open problem (the value problem for turn-based deterministic
mean-payoff games) that is not known to be solvable in polynomial time
IST Austria Technical Report
We consider the core algorithmic problems related to verification of systems with respect to three classical quantitative properties, namely, the mean-payoff property, the ratio property, and the minimum initial credit for energy property.
The algorithmic problem given a graph and a quantitative property asks to compute the optimal value (the infimum value over all traces) from every node of the graph. We consider graphs with constant treewidth, and it is well-known that the control-flow graphs of most programs have constant treewidth. Let denote the number of nodes of a graph, the number of edges (for constant treewidth graphs ) and the largest absolute value of the weights.
Our main theoretical results are as follows.
First, for constant treewidth graphs we present an algorithm that approximates the mean-payoff value within a multiplicative factor of in time and linear space, as compared to the classical algorithms that require quadratic time. Second, for the ratio property we present an algorithm that for constant treewidth graphs works in time , when the output is , as compared to the previously best known algorithm with running time . Third, for the minimum initial credit problem we show that (i)~for general graphs the problem can be solved in time and the associated decision problem can be solved in time, improving the previous known and bounds, respectively; and (ii)~for constant treewidth graphs we present an algorithm that requires time, improving the previous known bound.
We have implemented some of our algorithms and show that they present a significant speedup on standard benchmarks