882 research outputs found
Mean-payoff Automaton Expressions
Quantitative languages are an extension of boolean languages that assign to
each word a real number. Mean-payoff automata are finite automata with
numerical weights on transitions that assign to each infinite path the long-run
average of the transition weights. When the mode of branching of the automaton
is deterministic, nondeterministic, or alternating, the corresponding class of
quantitative languages is not robust as it is not closed under the pointwise
operations of max, min, sum, and numerical complement. Nondeterministic and
alternating mean-payoff automata are not decidable either, as the quantitative
generalization of the problems of universality and language inclusion is
undecidable.
We introduce a new class of quantitative languages, defined by mean-payoff
automaton expressions, which is robust and decidable: it is closed under the
four pointwise operations, and we show that all decision problems are decidable
for this class. Mean-payoff automaton expressions subsume deterministic
mean-payoff automata, and we show that they have expressive power incomparable
to nondeterministic and alternating mean-payoff automata. We also present for
the first time an algorithm to compute distance between two quantitative
languages, and in our case the quantitative languages are given as mean-payoff
automaton expressions
Non-Zero Sum Games for Reactive Synthesis
In this invited contribution, we summarize new solution concepts useful for
the synthesis of reactive systems that we have introduced in several recent
publications. These solution concepts are developed in the context of non-zero
sum games played on graphs. They are part of the contributions obtained in the
inVEST project funded by the European Research Council.Comment: LATA'16 invited pape
Minimizing Expected Cost Under Hard Boolean Constraints, with Applications to Quantitative Synthesis
In Boolean synthesis, we are given an LTL specification, and the goal is to
construct a transducer that realizes it against an adversarial environment.
Often, a specification contains both Boolean requirements that should be
satisfied against an adversarial environment, and multi-valued components that
refer to the quality of the satisfaction and whose expected cost we would like
to minimize with respect to a probabilistic environment.
In this work we study, for the first time, mean-payoff games in which the
system aims at minimizing the expected cost against a probabilistic
environment, while surely satisfying an -regular condition against an
adversarial environment. We consider the case the -regular condition is
given as a parity objective or by an LTL formula. We show that in general,
optimal strategies need not exist, and moreover, the limit value cannot be
approximated by finite-memory strategies. We thus focus on computing the
limit-value, and give tight complexity bounds for synthesizing
-optimal strategies for both finite-memory and infinite-memory
strategies.
We show that our game naturally arises in various contexts of synthesis with
Boolean and multi-valued objectives. Beyond direct applications, in synthesis
with costs and rewards to certain behaviors, it allows us to compute the
minimal sensing cost of -regular specifications -- a measure of quality
in which we look for a transducer that minimizes the expected number of signals
that are read from the input
Edit Distance for Pushdown Automata
The edit distance between two words is the minimal number of word
operations (letter insertions, deletions, and substitutions) necessary to
transform to . The edit distance generalizes to languages
, where the edit distance from to
is the minimal number such that for every word from
there exists a word in with edit distance at
most . We study the edit distance computation problem between pushdown
automata and their subclasses. The problem of computing edit distance to a
pushdown automaton is undecidable, and in practice, the interesting question is
to compute the edit distance from a pushdown automaton (the implementation, a
standard model for programs with recursion) to a regular language (the
specification). In this work, we present a complete picture of decidability and
complexity for the following problems: (1)~deciding whether, for a given
threshold , the edit distance from a pushdown automaton to a finite
automaton is at most , and (2)~deciding whether the edit distance from a
pushdown automaton to a finite automaton is finite.Comment: An extended version of a paper accepted to ICALP 2015 with the same
title. The paper has been accepted to the LMCS journa
The Theory of Universal Graphs for Infinite Duration Games
We introduce the notion of universal graphs as a tool for constructing
algorithms solving games of infinite duration such as parity games and mean
payoff games. In the first part we develop the theory of universal graphs, with
two goals: showing an equivalence and normalisation result between different
recently introduced related models, and constructing generic value iteration
algorithms for any positionally determined objective. In the second part we
give four applications: to parity games, to mean payoff games, and to
combinations of them (in the form of disjunctions of objectives). For each of
these four cases we construct algorithms achieving or improving over the best
known time and space complexity.Comment: 43 pages, 10 figure
Robust Multidimensional Mean-Payoff Games are Undecidable
Mean-payoff games play a central role in quantitative synthesis and
verification. In a single-dimensional game a weight is assigned to every
transition and the objective of the protagonist is to assure a non-negative
limit-average weight. In the multidimensional setting, a weight vector is
assigned to every transition and the objective of the protagonist is to satisfy
a boolean condition over the limit-average weight of each dimension, e.g.,
\LimAvg(x_1) \leq 0 \vee \LimAvg(x_2)\geq 0 \wedge \LimAvg(x_3) \geq 0. We
recently proved that when one of the players is restricted to finite-memory
strategies then the decidability of determining the winner is inter-reducible
with Hilbert's Tenth problem over rationals (a fundamental long-standing open
problem). In this work we allow arbitrary (infinite-memory) strategies for both
players and we show that the problem is undecidable
Quantitative Automata under Probabilistic Semantics
Automata with monitor counters, where the transitions do not depend on
counter values, and nested weighted automata are two expressive
automata-theoretic frameworks for quantitative properties. For a well-studied
and wide class of quantitative functions, we establish that automata with
monitor counters and nested weighted automata are equivalent. We study for the
first time such quantitative automata under probabilistic semantics. We show
that several problems that are undecidable for the classical questions of
emptiness and universality become decidable under the probabilistic semantics.
We present a complete picture of decidability for such automata, and even an
almost-complete picture of computational complexity, for the probabilistic
questions we consider
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