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  • 1
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Transactions on Automatic Control Vol. 66, No. 3 ( 2021-3), p. 1040-1054
    In: IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers (IEEE), Vol. 66, No. 3 ( 2021-3), p. 1040-1054
    Type of Medium: Online Resource
    ISSN: 0018-9286 , 1558-2523 , 2334-3303
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 241443-0
    detail.hit.zdb_id: 2033330-4
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  • 2
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Automatic Control Vol. 67, No. 12 ( 2022-12), p. 6333-6348
    In: IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers (IEEE), Vol. 67, No. 12 ( 2022-12), p. 6333-6348
    Type of Medium: Online Resource
    ISSN: 0018-9286 , 1558-2523 , 2334-3303
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2022
    detail.hit.zdb_id: 241443-0
    detail.hit.zdb_id: 2033330-4
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  • 3
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2022
    In:  Journal of the ACM Vol. 69, No. 6 ( 2022-12-31), p. 1-52
    In: Journal of the ACM, Association for Computing Machinery (ACM), Vol. 69, No. 6 ( 2022-12-31), p. 1-52
    Abstract: Arguing for the need to combine declarative and probabilistic programming, Bárány et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a “purely declarative probabilistic programming language.” We revisit this language and propose a more principled approach towards defining its semantics based on stochastic kernels and Markov processes—standard notions from probability theory. This allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by Bárány et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, LMCS 2022) and show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.
    Type of Medium: Online Resource
    ISSN: 0004-5411 , 1557-735X
    RVK:
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2006500-0
    detail.hit.zdb_id: 6759-3
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2006
    In:  International Journal on Software Tools for Technology Transfer Vol. 8, No. 6 ( 2006-10-27), p. 605-606
    In: International Journal on Software Tools for Technology Transfer, Springer Science and Business Media LLC, Vol. 8, No. 6 ( 2006-10-27), p. 605-606
    Type of Medium: Online Resource
    ISSN: 1433-2779 , 1433-2787
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2006
    detail.hit.zdb_id: 1481381-6
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 1996
    In:  Distributed Computing Vol. 9, No. 4 ( 1996-2), p. 157-171
    In: Distributed Computing, Springer Science and Business Media LLC, Vol. 9, No. 4 ( 1996-2), p. 157-171
    Type of Medium: Online Resource
    ISSN: 0178-2770 , 1432-0452
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 1996
    detail.hit.zdb_id: 1476700-4
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  • 6
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2023
    In:  Proceedings of the ACM on Programming Languages Vol. 7, No. POPL ( 2023-01-09), p. 1957-1986
    In: Proceedings of the ACM on Programming Languages, Association for Computing Machinery (ACM), Vol. 7, No. POPL ( 2023-01-09), p. 1957-1986
    Abstract: We develop a weakest-precondition-style calculus à la Dijkstra for reasoning about amortized expected runtimes of randomized algorithms with access to dynamic memory — the aert calculus. Our calculus is truly quantitative, i.e. instead of Boolean valued predicates, it manipulates real-valued functions. En route to the aert calculus, we study the ert calculus for reasoning about expected runtimes of Kaminski et al. [2018] extended by capabilities for handling dynamic memory, thus enabling compositional and local reasoning about randomized data structures . This extension employs runtime separation logic , which has been foreshadowed by Matheja [2020] and then implemented in Isabelle/HOL by Haslbeck [2021] . In addition to Haslbeck’s results, we further prove soundness of the so-extended ert calculus with respect to an operational Markov decision process model featuring countably-branching nondeterminism, provide extensive intuitive explanations, and provide proof rules enabling separation logic-style verification for upper bounds on expected runtimes. Finally, we build the so-called potential method for amortized analysis into the ert calculus, thus obtaining the aert calculus. Soundness of the aert calculus is obtained from the soundness of the ert calculus and some probabilistic form of telescoping. Since one needs to be able to handle changes in potential which can in principle be both positive or negative, the aert calculus needs to be — essentially — capable of handling certain signed random variables. A particularly pleasing feature of our solution is that, unlike e.g. Kozen [1985], we obtain a loop rule for our signed random variables, and furthermore, unlike e.g. Kaminski and Katoen [2017] , the aert calculus makes do without the need for involved technical machinery keeping track of the integrability of the random variables. Finally, we present case studies, including a formal analysis of a randomized delete-insert-find-any set data structure [Brodal et al. 1996], which yields a constant expected runtime per operation, whereas no deterministic algorithm can achieve this.
    Type of Medium: Online Resource
    ISSN: 2475-1421
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 2924207-1
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  • 7
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2019
    In:  Proceedings of the ACM on Programming Languages Vol. 3, No. POPL ( 2019-01-02), p. 1-29
    In: Proceedings of the ACM on Programming Languages, Association for Computing Machinery (ACM), Vol. 3, No. POPL ( 2019-01-02), p. 1-29
    Abstract: We present quantitative separation logic (QSL). In contrast to classical separation logic, QSL employs quantities which evaluate to real numbers instead of predicates which evaluate to Boolean values. The connectives of classical separation logic, separating conjunction and separating implication, are lifted from predicates to quantities. This extension is conservative: Both connectives are backward compatible to their classical analogs and obey the same laws, e.g. modus ponens, adjointness, etc. Furthermore, we develop a weakest precondition calculus for quantitative reasoning about probabilistic pointer programs in QSL. This calculus is a conservative extension of both Ishtiaq’s, O’Hearn’s and Reynolds’ separation logic for heap-manipulating programs and Kozen’s / McIver and Morgan’s weakest preexpectations for probabilistic programs. Soundness is proven with respect to an operational semantics based on Markov decision processes. Our calculus preserves O’Hearn’s frame rule , which enables local reasoning. We demonstrate that our calculus enables reasoning about quantities such as the probability of terminating with an empty heap, the probability of reaching a certain array permutation, or the expected length of a list.
    Type of Medium: Online Resource
    ISSN: 2475-1421
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2019
    detail.hit.zdb_id: 2924207-1
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  • 8
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2021
    In:  Proceedings of the ACM on Programming Languages Vol. 5, No. POPL ( 2021-01-04), p. 1-28
    In: Proceedings of the ACM on Programming Languages, Association for Computing Machinery (ACM), Vol. 5, No. POPL ( 2021-01-04), p. 1-28
    Abstract: Sensitivity properties describe how changes to the input of a program affect the output, typically by upper bounding the distance between the outputs of two runs by a monotone function of the distance between the corresponding inputs. When programs are probabilistic, the distance between outputs is a distance between distributions. The Kantorovich lifting provides a general way of defining a distance between distributions by lifting the distance of the underlying sample space; by choosing an appropriate distance on the base space, one can recover other usual probabilistic distances, such as the Total Variation distance. We develop a relational pre-expectation calculus to upper bound the Kantorovich distance between two executions of a probabilistic program. We illustrate our methods by proving algorithmic stability of a machine learning algorithm, convergence of a reinforcement learning algorithm, and fast mixing for card shuffling algorithms. We also consider some extensions: using our calculus to show convergence of Markov chains to the uniform distribution over states and an asynchronous extension to reason about pairs of program executions with different control flow.
    Type of Medium: Online Resource
    ISSN: 2475-1421
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2924207-1
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  • 9
    Online Resource
    Online Resource
    Centre pour la Communication Scientifique Directe (CCSD) ; 2014
    In:  Logical Methods in Computer Science Vol. Volume 10, Issue 3 ( 2014-09-10)
    In: Logical Methods in Computer Science, Centre pour la Communication Scientifique Directe (CCSD), Vol. Volume 10, Issue 3 ( 2014-09-10)
    Abstract: Markov automata (MAs) extend labelled transition systems with random delays and probabilistic branching. Action-labelled transitions are instantaneous and yield a distribution over states, whereas timed transitions impose a random delay governed by an exponential distribution. MAs are thus a nondeterministic variation of continuous-time Markov chains. MAs are compositional and are used to provide a semantics for engineering frameworks such as (dynamic) fault trees, (generalised) stochastic Petri nets, and the Architecture Analysis & Design Language (AADL). This paper considers the quantitative analysis of MAs. We consider three objectives: expected time, long-run average, and timed (interval) reachability. Expected time objectives focus on determining the minimal (or maximal) expected time to reach a set of states. Long-run objectives determine the fraction of time to be in a set of states when considering an infinite time horizon. Timed reachability objectives are about computing the probability to reach a set of states within a given time interval. This paper presents the foundations and details of the algorithms and their correctness proofs. We report on several case studies conducted using a prototypical tool implementation of the algorithms, driven by the MAPA modelling language for efficiently generating MAs.
    Type of Medium: Online Resource
    ISSN: 1860-5974
    Language: English
    Publisher: Centre pour la Communication Scientifique Directe (CCSD)
    Publication Date: 2014
    detail.hit.zdb_id: 2170262-7
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  • 10
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2015
    In:  Formal Methods in System Design Vol. 47, No. 2 ( 2015-10), p. 159-203
    In: Formal Methods in System Design, Springer Science and Business Media LLC, Vol. 47, No. 2 ( 2015-10), p. 159-203
    Type of Medium: Online Resource
    ISSN: 0925-9856 , 1572-8102
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2015
    detail.hit.zdb_id: 1479899-2
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