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Quantitative languages
"... Quantitative generalizations of classical languages, which assign to each word a real number instead of a boolean value, have applications in modeling resourceconstrained computation. We use weighted automata (finite automata with transition weights) to define several natural classes of quantitativ ..."
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Cited by 35 (14 self)
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Quantitative generalizations of classical languages, which assign to each word a real number instead of a boolean value, have applications in modeling resourceconstrained computation. We use weighted automata (finite automata with transition weights) to define several natural classes of quantitative languages over finite and infinite words; in particular, the real value of an infinite run is computed as the maximum, limsup, liminf, limit average, or discounted sum of the transition weights. We define the classical decision problems of automata theory (emptiness, universality, language inclusion, and language equivalence) in the quantitative setting and study their computational complexity. As the decidability of the languageinclusion problem remains open for some classes of weighted automata, we introduce a notion of quantitative simulation that is decidable and implies language inclusion. We also give a complete characterization of the expressive power of the various classes of weighted automata. In particular, we show that most classes of weighted
Better quality in synthesis through quantitative objectives
 In CoRR, abs/0904.2638
, 2009
"... Abstract. Most specification languages express only qualitative constraints. However, among two implementations that satisfy a given specification, one may be preferred to another. For example, if a specification asks that every request is followed by a response, one may prefer an implementation tha ..."
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Cited by 21 (9 self)
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Abstract. Most specification languages express only qualitative constraints. However, among two implementations that satisfy a given specification, one may be preferred to another. For example, if a specification asks that every request is followed by a response, one may prefer an implementation that generates responses quickly but does not generate unnecessary responses. We use quantitative properties to measure the “goodness ” of an implementation. Using games with corresponding quantitative objectives, we can synthesize “optimal ” implementations, which are preferred among the set of possible implementations that satisfy a given specification. In particular, we show how automata with lexicographic meanpayoff conditions can be used to express many interesting quantitative properties for reactive systems. In this framework, the synthesis of optimal implementations requires the solution of lexicographic meanpayoff games (for safety requirements), and the solution of games with both lexicographic meanpayoff and parity objectives (for liveness requirements). We present algorithms for solving both kinds of novel graph games. 1
Measuring and synthesizing systems in probabilistic environments
 CoRR
"... Abstract. Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is pre ..."
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Cited by 10 (6 self)
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Abstract. Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimalsynthesis problem: given an omegaregular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification under the given input assumption, synthesize a system that optimizes the measured value. For safety specifications and measures that are defined by meanpayoff automata, the optimalsynthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a longrun average reward objective, which can be done in polynomial time. For general omegaregular specifications, the solution rests on a new, polynomialtime algorithm for computing optimal strategies in MDPs with meanpayoff parity objectives. We present some experimental results showing optimal systems that were automatically generated in this way. 1
Expressiveness and closure properties for quantitative languages
 In Proc. of LICS: Logic in Computer Science. IEEE Comp. Soc
, 2009
"... Abstract. Weighted automata are nondeterministic automata with numerical weights on transitions. They can define quantitative languages L that assign to each word w a real number L(w). In the case of infinite words, the value of a run is naturally computed as the maximum, limsup, liminf, limit avera ..."
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Cited by 8 (5 self)
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Abstract. Weighted automata are nondeterministic automata with numerical weights on transitions. They can define quantitative languages L that assign to each word w a real number L(w). In the case of infinite words, the value of a run is naturally computed as the maximum, limsup, liminf, limit average, or discounted sum of the transition weights. We study expressiveness and closure questions about these quantitative languages. We first show that the set of words with value greater than a threshold can be nonωregular for deterministic limitaverage and discountedsum automata, while this set is always ωregular when the threshold is isolated (i.e., some neighborhood around the threshold contains no word). In the latter case, we prove that the ωregular language is robust against small perturbations of the transition weights. We next consider automata with transition weights 0 or 1 and show that they are as expressive as general weighted automata in the limitaverage case, but not in the discountedsum case. Third, for quantitative languages L1 and L2, we consider the operations max(L1, L2), min(L1, L2), and 1−L1, which generalize the boolean operations on languages, as well as the sum L1 +L2. We establish the closure properties of all classes of quantitative languages with respect to these four operations. 1
Deriving syntax and axioms for quantitative regular behaviours
, 2009
"... We present a systematic way to generate (1) languages of (generalised) regular expressions, and (2) sound and complete axiomatizations thereof, for a wide variety of quantitative systems. Our quantitative systems include weighted versions of automata and transition systems, in which transitions ar ..."
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Cited by 7 (4 self)
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We present a systematic way to generate (1) languages of (generalised) regular expressions, and (2) sound and complete axiomatizations thereof, for a wide variety of quantitative systems. Our quantitative systems include weighted versions of automata and transition systems, in which transitions are assigned a value in a monoid that represents cost, duration, probability, etc. Such systems are represented as coalgebras and (1) and (2) above are derived in a modular fashion from the underlying (functor) type of these coalgebras. In previous work, we applied a similar approach to a class of systems (without weights) that generalizes both the results of Kleene (on rational languages and DFA’s) and Milner (on regular behaviours and finite LTS’s), and includes many other systems such as Mealy and Moore machines. In the present paper, we extend this framework to deal with quantitative systems. As a consequence, our results now include languages and axiomatizations, both existing and new ones, for many different kinds of probabilistic systems.
Composition and Alternation for Weighted Automata
"... Abstract. Weighted automata are nondeterministic automata with numerical weights on transitions. They can be used to define quantitative languages L that assign to each (finite or infinite) word w a real number L(w). For instance, the value of an infinite run is computed as the maximum, limsup, limi ..."
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Cited by 7 (5 self)
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Abstract. Weighted automata are nondeterministic automata with numerical weights on transitions. They can be used to define quantitative languages L that assign to each (finite or infinite) word w a real number L(w). For instance, the value of an infinite run is computed as the maximum, limsup, liminf, limit average, or discounted sum of the transition weights. For quantitative languages L1, L2, we study the operations max(L1, L2), min(L1, L2), and 1 − L1 as natural generalizations of the boolean operations; we also consider the sum L1 + L2. We establish the closure properties of all classes of quantitative languages with respect to these four operations. We also introduce alternating weighted automata, give their closure properties, and compare the expressive power of the different classes of alternating and nondeterministic weighted automata. In particular, we show that alternation provides strictly more expressiveness than nondeterminism in the case of limitaverage and discountedsum automata. 1
Weak MSO with the unbounding Quantifier
"... A new class of languages of infinite words is introduced, called the maxregular languages, extending the class of ωregular languages. The class has two equivalent descriptions: in terms of automata (a type of deterministic counter automaton), and in terms of logic (weak monadic secondorder logic ..."
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Cited by 7 (2 self)
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A new class of languages of infinite words is introduced, called the maxregular languages, extending the class of ωregular languages. The class has two equivalent descriptions: in terms of automata (a type of deterministic counter automaton), and in terms of logic (weak monadic secondorder logic with a bounding quantifier). Effective translations between the logic and automata are given.
Energy and meanpayoff games with imperfect information
 In CSL 2010, volume LNCS 6247
, 2010
"... Abstract. We consider twoplayer games with imperfect information and quantitative objective. The game is played on a weighted graph with a state space partitioned into classes of indistinguishable states, giving players partial knowledge of the state. In an energy game, the weights represent resour ..."
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Cited by 7 (2 self)
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Abstract. We consider twoplayer games with imperfect information and quantitative objective. The game is played on a weighted graph with a state space partitioned into classes of indistinguishable states, giving players partial knowledge of the state. In an energy game, the weights represent resource consumption and the objective of the game is to maintain the sum of weights always nonnegative. In a meanpayoff game, the objective is to optimize the limitaverage usage of the resource. We show that the problem of determining if an energy game with imperfect information with fixed initial credit has a winning strategy is decidable, while the question of the existence of some initial credit such that the game has a winning strategy is undecidable. This undecidability result carries over to meanpayoff games with imperfect information. On the positive side, using a simple restriction on the game graph (namely, that the weights are visible), we show that these problems become EXPTIMEcomplete. 1
Whats decidable about weighted automata
 In Automated Technology for Verification and Analysis, Lecture Notes in Computer Science
, 2011
"... Abstract. Weighted automata map input words to numerical values. Applications of weighted automata include formal verification of quantitative properties, as well as text, speech, and image processing. A weighted automaton is defined with respect to a semiring. For the tropical semiring, the weight ..."
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Cited by 7 (4 self)
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Abstract. Weighted automata map input words to numerical values. Applications of weighted automata include formal verification of quantitative properties, as well as text, speech, and image processing. A weighted automaton is defined with respect to a semiring. For the tropical semiring, the weight of a run is the sum of the weights of the transitions taken along the run, and the value of a word is the minimal weight of an accepting run on it. In the 90’s, Krob studied the decidability of problems on rational series defined with respect to the tropical semiring. Rational series are strongly related to weighted automata, and Krob’s results apply to them. In particular, it follows from Krob’s results that the universality problem (that is, deciding whether the values of all words are below some threshold) is decidable for weighted automata defined with respect to the tropical semiring with domain ∪ {∞}, and that the equality problem is undecidable when the domain is ∪ {∞}. In this paper we continue the study of the borders of decidability in weighted automata, describe alternative and direct proofs of the above results, and tighten them further. Unlike the proofs of Krob, which are algebraic in their nature, our proofs stay in the terrain of state machines, and the reduction is from the halting problem of a twocounter machine. This enables us to significantly simplify Krob’s reasoning, make the undecidability result accessible to the automatatheoretic community, and strengthen it to apply already to a very simple class of automata: all the states are accepting, there are no initial nor final weights, and all the weights on the transitions are from the set {−1, 0, 1}. The fact we work directly with the automata enables us to tighten also the decidability results and to show that the universality problem for weighted automata defined with respect to the tropical semiring with domain ∪ {∞}, and ≥0 in fact even with domain ∪ {∞}, is PSPACEcomplete. Our results thus draw a sharper picture about the decidability of decision problems for weighted automata, in both the front of containment vs. universality and the front of the ∪ {∞} vs. the ∪ {∞} domains. 1