44 research outputs found

    Lazy Probabilistic Model Checking without Determinisation

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    The bottleneck in the quantitative analysis of Markov chains and Markov decision processes against specifications given in LTL or as some form of nondeterministic B\"uchi automata is the inclusion of a determinisation step of the automaton under consideration. In this paper, we show that full determinisation can be avoided: subset and breakpoint constructions suffice. We have implemented our approach---both explicit and symbolic versions---in a prototype tool. Our experiments show that our prototype can compete with mature tools like PRISM.Comment: 38 pages. Updated version for introducing the following changes: - general improvement on paper presentation; - extension of the approach to avoid full determinisation; - added proofs for such an extension; - added case studies; - updated old case studies to reflect the added extensio

    ISCASMC: A Web-Based Probabilistic Model Checker

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    We introduce the web-based model checker iscasMc for probabilistic systems (see http://iscasmc.ios.ac.cn/IscasMC). This Java application offers an easy-to-use web interface for the evaluation of Markov chains and decision processes against PCTL and PCTL specifications. Compared to PRISM or MRMC, iscasMc is particularly efficient in evaluating the probabilities of LTL properties. © 2014 Springer International Publishing Switzerland.We introduce the web-based model checker iscasMc for probabilistic systems (see http://iscasmc.ios.ac.cn/IscasMC). This Java application offers an easy-to-use web interface for the evaluation of Markov chains and decision processes against PCTL and PCTL specifications. Compared to PRISM or MRMC, iscasMc is particularly efficient in evaluating the probabilities of LTL properties. © 2014 Springer International Publishing Switzerland

    Complementing Semi-deterministic Buchi Automata

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    One Theorem to Rule Them All: A Unified Translation of LTL into {\omega}-Automata

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    We present a unified translation of LTL formulas into deterministic Rabin automata, limit-deterministic B\"uchi automata, and nondeterministic B\"uchi automata. The translations yield automata of asymptotically optimal size (double or single exponential, respectively). All three translations are derived from one single Master Theorem of purely logical nature. The Master Theorem decomposes the language of a formula into a positive boolean combination of languages that can be translated into {\omega}-automata by elementary means. In particular, Safra's, ranking, and breakpoint constructions used in other translations are not needed

    Statistical language models within the algebra of weighted rational languages

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    Statistical language models are an important tool in natural language processing. They represent prior knowledge about a certain language which is usually gained from a set of samples called a corpus. In this paper, we present a novel way of creating N-gram language models using weighted finite automata. The construction of these models is formalised within the algebra underlying weighted finite automata and expressed in terms of weighted rational languages and transductions. Besides the algebra we make use of five special constant weighted transductions which rely only on the alphabet and the model parameter N. In addition, we discuss efficient implementations of these transductions in terms of virtual constructions

    On the Succinctness of Good-for-MDPs Automata

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    Good-for-MDPs and good-for-games automata are two recent classes of nondeterministic automata that reside between general nondeterministic and deterministic automata. Deterministic automata are good-for-games, and good-for-games automata are good-for-MDPs, but not vice versa. One of the question this raises is how these classes relate in terms of succinctness. Good-for-games automata are known to be exponentially more succinct than deterministic automata, but the gap between good-for-MDPs and good-for-games automata as well as the gap between ordinary nondeterministic automata and those that are good-for-MDPs have been open. We establish that these gaps are exponential, and sharpen this result by showing that the latter gap remains exponential when restricting the nondeterministic automata to separating safety or unambiguous reachability automata.Comment: 18 page
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