18 research outputs found
How to Handle Assumptions in Synthesis
The increased interest in reactive synthesis over the last decade has led to
many improved solutions but also to many new questions. In this paper, we
discuss the question of how to deal with assumptions on environment behavior.
We present four goals that we think should be met and review several different
possibilities that have been proposed. We argue that each of them falls short
in at least one aspect.Comment: In Proceedings SYNT 2014, arXiv:1407.493
LNCS
We present the tool Quasy, a quantitative synthesis tool. Quasy takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. The user can choose between a system that satisfies and optimizes the specifications (a) under all possible environment behaviors or (b) under the most-likely environment behaviors given as a probability distribution on the possible input sequences. Quasy solves these two quantitative synthesis problems by reduction to instances of 2-player games and Markov Decision Processes (MDPs) with quantitative winning objectives. Quasy can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with mean-payoff objectives, and ergodic MDPs with mean-payoff parity objectives
LNCS
We define the model-measuring problem: given a model M and specification φ, what is the maximal distance ρ such that all models M′ within distance ρ from M satisfy (or violate) φ. The model measuring problem presupposes a distance function on models. We concentrate on automatic distance functions, which are defined by weighted automata. The model-measuring problem subsumes several generalizations of the classical model-checking problem, in particular, quantitative model-checking problems that measure the degree of satisfaction of a specification, and robustness problems that measure how much a model can be perturbed without violating the specification. We show that for automatic distance functions, and ω-regular linear-time and branching-time specifications, the model-measuring problem can be solved. We use automata-theoretic model-checking methods for model measuring, replacing the emptiness question for standard word and tree automata by the optimal-weight question for the weighted versions of these automata. We consider weighted automata that accumulate weights by maximizing, summing, discounting, and limit averaging. We give several examples of using the model-measuring problem to compute various notions of robustness and quantitative satisfaction for temporal specifications
Probably Safe or Live
This paper presents a formal characterisation of safety and liveness
properties \`a la Alpern and Schneider for fully probabilistic systems. As for
the classical setting, it is established that any (probabilistic tree) property
is equivalent to a conjunction of a safety and liveness property. A simple
algorithm is provided to obtain such property decomposition for flat
probabilistic CTL (PCTL). A safe fragment of PCTL is identified that provides a
sound and complete characterisation of safety properties. For liveness
properties, we provide two PCTL fragments, a sound and a complete one. We show
that safety properties only have finite counterexamples, whereas liveness
properties have none. We compare our characterisation for qualitative
properties with the one for branching time properties by Manolios and Trefler,
and present sound and complete PCTL fragments for characterising the notions of
strong safety and absolute liveness coined by Sistla
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
Synthesizing Systems with Optimal Average-Case Behavior for Ratio Objectives
We show how to automatically construct a system that satisfies a given
logical specification and has an optimal average behavior with respect to a
specification with ratio costs.
When synthesizing a system from a logical specification, it is often the case
that several different systems satisfy the specification. In this case, it is
usually not easy for the user to state formally which system she prefers. Prior
work proposed to rank the correct systems by adding a quantitative aspect to
the specification. A desired preference relation can be expressed with (i) a
quantitative language, which is a function assigning a value to every possible
behavior of a system, and (ii) an environment model defining the desired
optimization criteria of the system, e.g., worst-case or average-case optimal.
In this paper, we show how to synthesize a system that is optimal for (i) a
quantitative language given by an automaton with a ratio cost function, and
(ii) an environment model given by a labeled Markov decision process. The
objective of the system is to minimize the expected (ratio) costs. The solution
is based on a reduction to Markov Decision Processes with ratio cost functions
which do not require that the costs in the denominator are strictly positive.
We find an optimal strategy for these using a fractional linear program.Comment: In Proceedings iWIGP 2011, arXiv:1102.374
IST Austria Technical Report
We define the model-measuring problem: given a model M and specification φ, what is the maximal distance ρ such that all models M'within distance ρ from M satisfy (or violate)φ. The model measuring problem presupposes a distance function on models. We concentrate on automatic distance functions, which are defined by weighted automata.
The model-measuring problem subsumes several generalizations of the classical model-checking problem, in particular, quantitative model-checking problems that measure the degree of satisfaction of a specification, and robustness problems that measure how much a model can be perturbed without violating the specification.
We show that for automatic distance functions, and ω-regular linear-time and branching-time specifications, the model-measuring problem can be solved.
We use automata-theoretic model-checking methods for model measuring, replacing the emptiness question for standard word and tree automata by the optimal-weight question for the weighted versions of these automata. We consider weighted automata that accumulate weights by maximizing, summing, discounting, and limit averaging.
We give several examples of using the model-measuring problem to compute various notions of robustness and quantitative satisfaction for temporal specifications
LNCS
We present an algorithmic method for the quantitative, performance-aware synthesis of concurrent programs. The input consists of a nondeterministic partial program and of a parametric performance model. The nondeterminism allows the programmer to omit which (if any) synchronization construct is used at a particular program location. The performance model, specified as a weighted automaton, can capture system architectures by assigning different costs to actions such as locking, context switching, and memory and cache accesses. The quantitative synthesis problem is to automatically resolve the nondeterminism of the partial program so that both correctness is guaranteed and performance is optimal. As is standard for shared memory concurrency, correctness is formalized "specification free", in particular as race freedom or deadlock freedom. For worst-case (average-case) performance, we show that the problem can be reduced to 2-player graph games (with probabilistic transitions) with quantitative objectives. While we show, using game-theoretic methods, that the synthesis problem is Nexp-complete, we present an algorithmic method and an implementation that works efficiently for concurrent programs and performance models of practical interest. We have implemented a prototype tool and used it to synthesize finite-state concurrent programs that exhibit different programming patterns, for several performance models representing different architectures
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