Thematische Bibliographien / Synthesis of Probabilistic Programs

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Auswahl der wissenschaftlichen Literatur zum Thema „Synthesis of Probabilistic Programs“

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Veröffentlicht am 28. Juni 2021

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Zeitschriftenartikel zum Thema "Synthesis of Probabilistic Programs"

Nori, Aditya V., Sherjil Ozair, Sriram K. Rajamani und Deepak Vijaykeerthy. „Efficient synthesis of probabilistic programs“. ACM SIGPLAN Notices 50, Nr. 6 (07.08.2015): 208–17. http://dx.doi.org/10.1145/2813885.2737982.

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Salustowicz, Rafal, und Jürgen Schmidhuber. „Probabilistic Incremental Program Evolution“. Evolutionary Computation 5, Nr. 2 (Juni 1997): 123–41. http://dx.doi.org/10.1162/evco.1997.5.2.123.

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Probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinements depend only on the best program of the current population, PIPE can evaluate program populations efficiently when the goal is to discover a program with minimal runtime. We compare PIPE to GP on a function regression problem and the 6-bit parity problem. We also use PIPE to solve tasks in partially observable mazes, where the best programs have minimal runtime.

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Saad, Feras A., Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard und Vikash K. Mansinghka. „Bayesian synthesis of probabilistic programs for automatic data modeling“. Proceedings of the ACM on Programming Languages 3, POPL (02.01.2019): 1–32. http://dx.doi.org/10.1145/3290350.

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Satake, Yuki, Hiroshi Unno und Hinata Yanagi. „Probabilistic Inference for Predicate Constraint Satisfaction“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 02 (03.04.2020): 1644–51. http://dx.doi.org/10.1609/aaai.v34i02.5526.

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In this paper, we present a novel constraint solving method for a class of predicate Constraint Satisfaction Problems (pCSP) where each constraint is represented by an arbitrary clause of first-order predicate logic over predicate variables. The class of pCSP properly subsumes the well-studied class of Constrained Horn Clauses (CHCs) where each constraint is restricted to a Horn clause. The class of CHCs has been widely applied to verification of linear-time safety properties of programs in different paradigms. In this paper, we show that pCSP further widens the applicability to verification of branching-time safety properties of programs that exhibit finitely-branching non-determinism. Solving pCSP (and CHCs) however is challenging because the search space of solutions is often very large (or unbounded), high-dimensional, and non-smooth. To address these challenges, our method naturally combines techniques studied separately in different literatures: counterexample guided inductive synthesis (CEGIS) and probabilistic inference in graphical models. We have implemented the presented method and obtained promising results on existing benchmarks as well as new ones that are beyond the scope of existing CHC solvers.

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Lee, Woosuk, Kihong Heo, Rajeev Alur und Mayur Naik. „Accelerating search-based program synthesis using learned probabilistic models“. ACM SIGPLAN Notices 53, Nr. 4 (02.12.2018): 436–49. http://dx.doi.org/10.1145/3296979.3192410.

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Kemper, C. A., N. M. Lane, R. W. Carlson, M. A. Musen und S. W. Tu. „A Methodology for Determining Patients’ Eligibility for Clinical Trials“. Methods of Information in Medicine 32, Nr. 04 (1993): 317–25. http://dx.doi.org/10.1055/s-0038-1634933.

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AbstractThe task of determining patients’ eligibility for clinical trials is knowledge and data intensive. In this paper, we present a model for the task of eligibility determination, and describe how a computer system can assist clinical researchers in performing that task. Qualitative and probabilistic approaches to computing and summarizing the eligibility status of potentially eligible patients are described. The two approaches are compared, and a synthesis that draws on the strengths of each approach is proposed. The result of applying these techniques to a database of HIV-positive patient cases suggests that computer programs such as the one described can increase the accrual rate of eligible patients into clinical trials. These methods may also be applied to the task of determining from electronic patient records whether practice guidelines apply in particular clinical situations.

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Chakraborty, Sourav, und Kuldeep S. Meel. „On Testing of Uniform Samplers“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 7777–84. http://dx.doi.org/10.1609/aaai.v33i01.33017777.

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Recent years have seen an unprecedented adoption of artificial intelligence in a wide variety of applications ranging from medical diagnosis, automobile industry, security to aircraft collision avoidance. Probabilistic reasoning is a key component of such modern artificial intelligence systems. Sampling techniques form the core of the state of the art probabilistic reasoning systems. The divide between the existence of sampling techniques that have strong theoretical guarantees but fail to scale and scalable techniques with weak or no theoretical guarantees mirrors the gap in software engineering between poor scalability of classical program synthesis techniques and billions of programs that are routinely used by practitioners. One bridge connecting the two extremes in the context of software engineering has been program testing. In contrast to testing for deterministic programs, where one trace is sufficient to prove the existence of a bug, in case of samplers one sample is typically not sufficient to prove non-conformity of the sampler to the desired distribution. This makes one wonder whether it is possible to design testing methodology to test whether a sampler under test generates samples close to a given distribution. The primary contribution of this paper is an affirmative answer to the above question when the given distribution is a uniform distribution: We design, to the best of our knowledge, the first algorithmic framework, Barbarik, to test whether the distribution generated is ε−close or η−far from the uniform distribution. In contrast to the sampling techniques that require an exponential or sub-exponential number of samples for sampler whose support can be represented by n bits, Barbarik requires only O(1/(η−ε)4) samples. We present a prototype implementation of Barbarik and use it to test three state of the art uniform samplers over the support defined by combinatorial constraints. Barbarik can provide a certificate of uniformity to one sampler and demonstrate nonuniformity for the other two samplers.

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Dekhtyar, Alex, und V. S. Subrahmanian. „Hybrid probabilistic programs“. Journal of Logic Programming 43, Nr. 3 (Juni 2000): 187–250. http://dx.doi.org/10.1016/s0743-1066(99)00059-x.

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Dix, Jürgen, Mirco Nanni und V. S. Subrahmanian. „Probabilistic agent programs“. ACM Transactions on Computational Logic 1, Nr. 2 (Oktober 2000): 208–46. http://dx.doi.org/10.1145/359496.359508.

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Hur, Chung-Kil, Aditya V. Nori, Sriram K. Rajamani und Selva Samuel. „Slicing probabilistic programs“. ACM SIGPLAN Notices 49, Nr. 6 (05.06.2014): 133–44. http://dx.doi.org/10.1145/2666356.2594303.

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Dissertationen zum Thema "Synthesis of Probabilistic Programs"

Escalante, Marco Antonio. „Probabilistic timing verification and timing analysis for synthesis of digital interface controllers“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0023/NQ36637.pdf.

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Gretz, Friedrich Verfasser], Joost-Pieter [Akademischer Betreuer] [Katoen und Sriram [Akademischer Betreuer] Sankaranarayanan. „Semantics and loop invariant synthesis for probabilistic programs / Friedrich Gretz ; Joost-Pieter Katoen, Sriram Sankaranarayanan“. Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1126278491/34.

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Gretz, Friedrich [Verfasser], Joost-Pieter [Akademischer Betreuer] Katoen und Sriram [Akademischer Betreuer] Sankaranarayanan. „Semantics and loop invariant synthesis for probabilistic programs / Friedrich Gretz ; Joost-Pieter Katoen, Sriram Sankaranarayanan“. Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1126278491/34.

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Schoner, Bernd 1969. „Probabilistic characterization and synthesis of complex driven systems“. Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62352.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.
Includes bibliographical references (leaves 194-204).
Real-world systems that have characteristic input-output patterns but don't provide access to their internal states are as numerous as they are difficult to model. This dissertation introduces a modeling language for estimating and emulating the behavior of such systems given time series data. As a benchmark test, a digital violin is designed from observing the performance of an instrument. Cluster-weighted modeling (CWM), a mixture density estimator around local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. Furthermore a real-time implementation, a Hidden-Markov architecture, and function approximation under constraints are derived within the framework. CWM is then applied in the context of different problems and data sets, leading to architectures such as cluster-weighted classification, cluster-weighted estimation, and cluster-weighted sampling. Each application relies on a specific data representation, specific pre and post-processing algorithms, and a specific hybrid of CWM. The third part of this thesis introduces data-driven modeling of acoustic instruments, a novel technique for audio synthesis. CWM is applied along with new sensor technology and various audio representations to estimate models of violin-family instruments. The approach is demonstrated by synthesizing highly accurate violin sounds given off-line input data as well as cello sounds given real-time input data from a cello player.
by Bernd Schoner.
Ph.D.

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Stupinský, Šimon. „Pokročilé metody pro syntézu pravděpodobnostních programů“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445587.

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Pravdepodobnostné programy zohrávajú rozhodujúcu úlohu v rôznych technických doménach, ako napríklad počítačové siete, vstavané systémy, stratégie riadenia spotreby energie alebo softvérové produčkné linky. PAYNT je nástroj na automatizovanú syntézu pravdepodobnostných programov vyhovujúcich zadaným špecifikáciam. V tejto práci rozširujeme tento nástroj predovšetkým o podporu optimálnej syntézy a syntézy viacerých špecifikácií. Ďalej sme navrhli a implementovali novú metódu, ktorá dokáže efektívne syntetizovať parametre so spojitým definičným oborom ovplyvňujúce pravdepodobnostné prechody popri syntéze topológie programov, t.j., podporu pre syntézu topológie aj parametrov súčasne. Demonštrujeme užitočnosť a výkonnosť nástroja PAYNT na širokej škále prípadových štúdií z rôznych aplikačných domén ktoré majú uplatnenie v reálnom svete. Pri náročných problémoch syntézy môže PAYNT výrazne znížiť dobu behu až z dní na minúty a zároveň zaistiť úplnosť procesu syntézy.

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Marcin, Vladimír. „GPU-akcelerovná syntéza pravděpodobnostních programů“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445566.

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V tejto práci sa zoberáme problémom automatizovanej syntézy pravdepodobnostných programov: majme konečnú rodinu kandidátnych programov, v ktorej chceme efektívne identifikovať program spĺňajúci danú špecifikáciu. Aj riešenie tých najjednoduchších syntéznych problémov v praxi predstavuje NP-ťažký problém. Pokrok v tejto oblasti prináša nástroj Paynt, ktorý na riešenie tohto problému používa novú integrovanú metódu syntézy pravdepodobnostných programov. Aj keď sa tento prístup dokáže efektívne vysporiadať s exponenciálnym rastom rodín kandidátnych riešení, stále tu existuje problém spôsobený exponenciálnym rastom jednotlivých členov týchto rodín. S cieľom vysporiadať sa aj s týmto problémom, sme implementovali GPU orientované algoritmy slúžiace na overovanie kandidátnych programov (modelov), ktoré danú úlohu paralelizujú na stavovej úrovni pravdepodobnostých modelov. Celkové zrýchlenie doshiahnuté týmto prístupom za určitých podmienok potom prinieslo takmer teoretický limit možného zrýchlenia syntézneho procesu.

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Angelopoulos, Nicos. „Probabilistic finite domains“. Thesis, City University London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342823.

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Faria, Francisco Henrique Otte Vieira de. „Learning acyclic probabilistic logic programs from data“. Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-27022018-090821/.

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To learn a probabilistic logic program is to find a set of probabilistic rules that best fits some data, in order to explain how attributes relate to one another and to predict the occurrence of new instantiations of these attributes. In this work, we focus on acyclic programs, because in this case the meaning of the program is quite transparent and easy to grasp. We propose that the learning process for a probabilistic acyclic logic program should be guided by a scoring function imported from the literature on Bayesian network learning. We suggest novel techniques that lead to orders of magnitude improvements in the current state-of-art represented by the ProbLog package. In addition, we present novel techniques for learning the structure of acyclic probabilistic logic programs.
O aprendizado de um programa lógico probabilístico consiste em encontrar um conjunto de regras lógico-probabilísticas que melhor se adequem aos dados, a fim de explicar de que forma estão relacionados os atributos observados e predizer a ocorrência de novas instanciações destes atributos. Neste trabalho focamos em programas acíclicos, cujo significado é bastante claro e fácil de interpretar. Propõe-se que o processo de aprendizado de programas lógicos probabilísticos acíclicos deve ser guiado por funções de avaliação importadas da literatura de aprendizado de redes Bayesianas. Neste trabalho s~ao sugeridas novas técnicas para aprendizado de parâmetros que contribuem para uma melhora significativa na eficiência computacional do estado da arte representado pelo pacote ProbLog. Além disto, apresentamos novas técnicas para aprendizado da estrutura de programas lógicos probabilísticos acíclicos.

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Paige, Timothy Brooks. „Automatic inference for higher-order probabilistic programs“. Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d912c4de-4b08-4729-aa19-766413735e2a.

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Probabilistic models used in quantitative sciences have historically co-evolved with methods for performing inference: specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. The intertwined nature of modeling and computational concerns leaves much of the promise of probabilistic modeling out of reach for data scientists, forcing practitioners to turn to off-the-shelf solutions. The emerging field of probabilistic programming aims to reduce the technical and cognitive overhead for writing and designing novel probabilistic models, by introducing a specialized programming language as an abstraction barrier between modeling and inference. The aim of this thesis is to develop inference algorithms that scale well and are applicable to broad model families. We focus particularly on methods that can be applied to models written in general-purpose higher-order probabilistic programming languages, where programs may make use of recursion, arbitrary deterministic simulation, and higher-order functions to create more accurate models of an application domain. In a probabilistic programming system, probabilistic models are defined using a modeling language; a backend implements generic inference methods applicable to any model written in this language. Probabilistic programs - models - can be written without concern for how inference will later be performed. We begin by considering several existing probabilistic programming languages, their design choices, and tradeoffs. We then demonstrate how programs written in higher-order languages can be used to define coherent probability models, describing possible approaches to inference, and providing explicit algorithms for efficient implementations of both classic and novel inference methods based on and extending sequential Monte Carlo. This is followed by an investigation into the use of variational inference methods within higher-order probabilistic programming languages, with application to policy learning, adaptive importance sampling, and amortization of inference.

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Crubillé, Raphaëlle. „Behavioural distances for probabilistic higher-order programs“. Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC084.

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Cette thèse est consacrée à l’étude d’équivalences et de distances comportementales destinées à comparer des programmes probabilistes d’ordre supérieur. Le manuscrit est divisé en trois parties. La première partie consiste en une présentation des langages probabilistes d’ordre supérieur, et des notions d’équivalence et de distance contextuelles pour de tels langages.Dans une deuxième partie, on suit une approche opérationnelle pour construire des notions d’équivalences et de métriques plus simples à manipuler que les notions contextuelles : on prend comme point de départ les deux équivalences comportementales pour le lambda-calcul probabiliste équipé d’une stratégie d’évaluation basée sur l’appel par nom introduites par Dal Lago, Sangiorgi and Alberti : ces derniers définissent deux équivalences–la trace équivalence, et la bisimulation probabiliste, et montrent que pour ce langage, la trace équivalence permet de complètement caractériser–i.e. est pleinement abstraite– l’équivalence contextuelle, tandis que la bisimulation probabiliste est une approximation correcte de l’équivalence contextuelle, mais n’est pas pleinement abstraite. Dans la partie opérationnelle de cette thèse, on montre que la bisimulation probabiliste redevient pleinement abstraite quand on remplace la stratégie d’évaluation par nom par une stratégie d’évaluation par valeur. Le reste de cette partie est consacrée à une généralisation quantitative de la trace équivalence, i.e. une trace distance sur les programmes. On introduit d’abord une trace distance pour un λ-calcul probabiliste affine, i.e. où le contexte peut utiliser son argument au plus une fois, et ensuite pour un λ-calcul probabiliste où les contextes ont la capacité de copier leur argument; dans ces deux cas, on montre que les distances traces obtenues sont pleinement abstraites.La troisième partie considère deux modèles dénotationnels de langages probabilistes d’ordre supérieur : le modèle des espaces cohérents probabiliste, dû à Danos et Ehrhard, qui interprète le langage obtenu en équipant PCF avec des probabilités discrètes, et le modèle des cônes mesurables et des fonctions stables et mesurables, développé plus récemment par Ehrhard, Pagani and Tasson pour le langage obtenu en enrichissant PCF avec des probabilités continues. Cette thèse établit deux résultats sur la structure de ces modèles. On montre d’abord que l’exponentielle de la catégorie des espaces cohérents peut être exprimée en utilisant le comonoide commutatif libre : il s’agit d’un résultat de généricité de cette catégorie vue comme un modèle de la logique linéaire. Le deuxième résultat éclaire les liens entre ces deux modèles : on montre que la catégorie des cônes mesurables et des fonctions stables et mesurable est une extension conservatrice de la catégorie de co-Kleisli des espaces cohérents probabilistes. Cela signifie que le modèle récemment introduit par Ehrhard, Pagani et Tasson peut être vu comme la généralisation au cas continu du modèle de PCF équipé avec des probabilités discrètes dans les espaces cohérents probabilistes
The present thesis is devoted to the study of behavioral equivalences and distances for higher-order probabilistic programs. The manuscript is divided into three parts. In the first one, higher-order probabilistic languages are presented, as well as how to compare such programs with context equivalence and context distance.The second part follows an operational approach in the aim of building equivalences and metrics easier to handle as their contextual counterparts. We take as starting point here the two behavioral equivalences introduced by Dal Lago, Sangiorgi and Alberti for the probabilistic lambda-calculus equipped with a call-by-name evaluation strategy: the trace equivalence and the bisimulation equivalence. These authors showed that for their language, trace equivalence completely characterizes context equivalence—i.e. is fully abstract, while probabilistic bisimulation is a sound approximation of context equivalence, but is not fully abstract. In the operational part of the present thesis, we show that probabilistic bisimulation becomes fully abstract when we replace the call-by-name paradigm by the call-by-value one. The remainder of this part is devoted to a quantitative generalization of trace equivalence, i.e. a trace distance on programs. We introduce first e trace distance for an affine probabilistic lambda-calculus—i.e. where a function can use its argument at most once, and then for a more general probabilistic lambda-calculus where functions have the ability to duplicate their arguments. In these two cases, we show that these trace distances are fully abstract.In the third part, two denotational models of higher-order probabilistic languages are considered: the Danos and Ehrhard's model based on probabilistic coherence spaces that interprets the language PCF enriched with discrete probabilities, and the Ehrhard, Pagani and Tasson's one based on measurable cones and measurable stable functions that interpret PCF equipped with continuous probabilities. The present thesis establishes two results on these models structure. We first show that the exponential comonad of the category of probabilistic coherent spaces can be expressed using the free commutative comonoid: it consists in a genericity result for this category seen as a model of Linear Logic. The second result clarify the connection between these two models: we show that the category of measurable cones and measurable stable functions is a conservative extension of the co-Kleisli category of probabilistic coherent spaces. It means that the recently introduced model of Ehrhard, Pagani and Tasson can be seen as the generalization to the continuous case of the model for PCF with discrete probabilities in probabilistic coherent spaces

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Bücher zum Thema "Synthesis of Probabilistic Programs"

Brown, Andrew M. Probabilistic component mode synthesis of nondeterministic substructures. Washington, DC: [National Aeronautics and Space Administration, 1997.

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Schmid, Ute. Inductive Synthesis of Functional Programs. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b12055.

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Kwan, Victor. A predicative model for probabilistic specifications and programs. Ottawa: National Library of Canada, 1998.

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Speight, Vanessa. Probabilistic modeling framework for assessing water quality sampling programs. Denver, Colo: Water Research Foundation, 2009.

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Pai, Shantaram S. Probabilistic structural analysis of adaptive/smart/intelligent space structures. [Washington, DC]: National Aeronautics and Space Administration, 1991.

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United Kingdom. Department of Social Security. Social Assistance in OECD countries: Synthesis report. London: HMSO, 1996.

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Bunt, Harry. Advances in Probabilistic and Other Parsing Technologies. Dordrecht: Springer Netherlands, 2000.

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Pai, Shantaram S. Probabilistic structural analysis of a truss typical for space station. [Washington, DC]: National Aeronautics and Space Administration, 1990.

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David, Cope. Virtual music: Computer synthesis of musical style. Cambridge, Mass: MIT Press, 2001.

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Schroeder, Manfred R. Computer Speech: Recognition, Compression, Synthesis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Buchteile zum Thema "Synthesis of Probabilistic Programs"

Andriushchenko, Roman, Milan Češka, Sebastian Junges, Joost-Pieter Katoen und Šimon Stupinský. „PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs“. In Computer Aided Verification, 856–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_40.

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AbstractThis paper presents PAYNT, a tool to automatically synthesise probabilistic programs. PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch representing a finite family of program candidates. A tight interaction between inductive oracle-guided methods with state-of-the-art probabilistic model checking is at the heart of PAYNT. These oracle-guided methods effectively reason about all possible candidates and synthesise programs that meet a given specification formulated as a conjunction of temporal logic constraints and possibly including an optimising objective. We demonstrate the performance and usefulness of PAYNT using several case studies from different application domains; e.g., we find the optimal randomized protocol for network stabilisation among 3M potential programs within minutes, whereas alternative approaches would need days to do so.

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Sneyers, Jon, und Danny De Schreye. „Probabilistic Termination of CHRiSM Programs“. In Logic-Based Program Synthesis and Transformation, 221–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32211-2_15.

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Klinkenberg, Lutz, Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Joshua Moerman und Tobias Winkler. „Generating Functions for Probabilistic Programs“. In Logic-Based Program Synthesis and Transformation, 231–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68446-4_12.

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Andriushchenko, Roman, Milan Češka, Sebastian Junges und Joost-Pieter Katoen. „Inductive Synthesis for Probabilistic Programs Reaches New Horizons“. In Tools and Algorithms for the Construction and Analysis of Systems, 191–209. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_11.

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AbstractThis paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a reachability specification. The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family. These CEs leverage the semantics of the family in the form of bounds on its best- and worst-case behaviour provided by a deductive oracle using an MDP abstraction. The method further monitors the performance of the synthesis and adaptively switches between inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks. For challenging problems, such as the synthesis of decentralized partially-observable controllers, we reduce the run-time from a day to minutes.

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Chasins, Sarah, und Phitchaya Mangpo Phothilimthana. „Data-Driven Synthesis of Full Probabilistic Programs“. In Computer Aided Verification, 279–304. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63387-9_14.

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Abate, Alessandro, Mirco Giacobbe und Diptarko Roy. „Learning Probabilistic Termination Proofs“. In Computer Aided Verification, 3–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_1.

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AbstractWe present the first machine learning approach to the termination analysis of probabilistic programs. Ranking supermartingales (RSMs) prove that probabilistic programs halt, in expectation, within a finite number of steps. While previously RSMs were directly synthesised from source code, our method learns them from sampled execution traces. We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source code using satisfiability modulo theories (SMT); if the latter step produces a counterexample, we generate from it new sample traces and repeat learning in a counterexample-guided inductive synthesis loop, until the SMT solver confirms the validity of the RSM. The result is thus a sound witness of probabilistic termination. Our learning strategy is agnostic to the source code and its verification counterpart supports the widest range of probabilistic single-loop programs that any existing tool can handle to date. We demonstrate the efficacy of our method over a range of benchmarks that include linear and polynomial programs with discrete, continuous, state-dependent, multi-variate, hierarchical distributions, and distributions with undefined moments.

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Češka, Milan, Christian Hensel, Sebastian Junges und Joost-Pieter Katoen. „Counterexample-Driven Synthesis for Probabilistic Program Sketches“. In Lecture Notes in Computer Science, 101–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30942-8_8.

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Katoen, Joost-Pieter, Friedrich Gretz, Nils Jansen, Benjamin Lucien Kaminski und Federico Olmedo. „Understanding Probabilistic Programs“. In Lecture Notes in Computer Science, 15–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23506-6_4.

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Popescu, Andrei, Johannes Hölzl und Tobias Nipkow. „Formalizing Probabilistic Noninterference“. In Certified Programs and Proofs, 259–75. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03545-1_17.

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De Raedt, Luc, Kristian Kersting, Angelika Kimmig, Kate Revoredo und Hannu Toivonen. „Revising Probabilistic Prolog Programs“. In Inductive Logic Programming, 30–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73847-3_7.

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Konferenzberichte zum Thema "Synthesis of Probabilistic Programs"

Nori, Aditya V., Sherjil Ozair, Sriram K. Rajamani und Deepak Vijaykeerthy. „Efficient synthesis of probabilistic programs“. In PLDI '15: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2737924.2737982.

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Zhang, Yating, Wei Dong, Daiyan Wang, Jiaxin Liu und Binbin Liu. „Probabilistic Synthesis for Program with Non-API Operations“. In 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2020. http://dx.doi.org/10.1109/qrs-c51114.2020.00082.

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Tenenbaum, Joshua. „Reverse-engineering core common sense with the tools of probabilistic programs, game-style simulation engines, and inductive program synthesis“. In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3466000.

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Lee, Woosuk, Kihong Heo, Rajeev Alur und Mayur Naik. „Accelerating search-based program synthesis using learned probabilistic models“. In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192366.3192410.

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Spieler, Stefan, Stephan Staudacher, Roland Fiola, Peter Sahm und Matthias Weißschuh. „Probabilistic Engine Performance Scatter and Deterioration Modeling“. In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27051.

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The change of performance parameters over time due to engine deterioration and production scatter plays an important role to ensure safe and economical engine operation. A tool has been developed which is able to model production scatter and engine deterioration on the basis of elementary changes of numerous construction features. In order to consider the characteristics of an engine fleet as well as random environmental influences, a probabilistic approach using Monte Carlo Simulation (MCS) was chosen. To quantify the impact of feature deviations on performance relevant metrics, non-linear sensitivity functions are used to obtain scalars and offsets on turbomachinery maps which reflect module behavior during operation. Probability density functions (PDFs) of user-defined performance parameters of an engine fleet are then calculated by performing an MCS in a performance synthesis program. For the validation of the developed methodology pass-off test data, endurance engine test data, as well as data from engine maintenance incoming tests have been used. For this purpose, measured engine fleet performance data have been corrected by statistically eliminating the influence of measuring errors. The validation process showed the model’s ability to predict more than 90% of the measured performance variance. Furthermore, predicted performance trends correspond well to performance data from engines in operation. Two model enhancements are presented, the first of which is intended for maintenance cost prediction. It is able to generate PDFs of failure times for different features. The second enhancement correlates feature change and operating conditions and thus connects airline operation and maintenance costs. Subsequently, it is shown that the model developed is a powerful tool to assist in aircraft engine design and production processes thanks to its ability to identify and quantitatively assess main drivers for performance variance and trends.

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Nandi, Chandrakana, Dan Grossman, Adrian Sampson, Todd Mytkowicz und Kathryn S. McKinley. „Debugging probabilistic programs“. In PLDI '17: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3088525.3088564.

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Hur, Chung-Kil, Aditya V. Nori, Sriram K. Rajamani und Selva Samuel. „Slicing probabilistic programs“. In PLDI '14: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2594291.2594303.

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Sankaranarayanan, Sriram, Aleksandar Chakarov und Sumit Gulwani. „Static analysis for probabilistic programs“. In the 34th ACM SIGPLAN conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491956.2462179.

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Cusumano-Towner, Marco, Benjamin Bichsel, Timon Gehr, Martin Vechev und Vikash K. Mansinghka. „Incremental inference for probabilistic programs“. In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192366.3192399.

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Olmedo, Federico, Benjamin Lucien Kaminski, Joost-Pieter Katoen und Christoph Matheja. „Reasoning about Recursive Probabilistic Programs“. In LICS '16: 31st Annual ACM/IEEE Symposium on Logic in Computer Science. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2933575.2935317.

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Berichte der Organisationen zum Thema "Synthesis of Probabilistic Programs"

Dantzig, G. B., und G. Infanger. A probabilistic lower bound for two-stage stochastic programs. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/656786.

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Torres, Marissa, Norberto Nadal-Caraballo und Alexandros Taflanidis. Rapid tidal reconstruction for the Coastal Hazards System and StormSim part II : Puerto Rico and U.S. Virgin Islands. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41482.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the continuing efforts towards incorporating rapid tidal time-series reconstruction and prediction capabilities into the Coastal Hazards System (CHS) and the Stochastic Storm Simulation System (StormSim). The CHS (Nadal-Caraballo et al. 2020) is a national effort for the quantification of coastal storm hazards, including a database and web tool (https://chs.erdc.dren.mil) for the deployment of results from the Probabilistic Coastal Hazard Analysis (PCHA) framework. These PCHA products are developed from regional studies such as the North Atlantic Coast Comprehensive Study (NACCS) (Nadal-Caraballo et al. 2015; Cialone et al. 2015) and the ongoing South Atlantic Coast Study (SACS). The PCHA framework considers hazards due to both tropical and extratropical cyclones, depending on the storm climatology of the region of interest. The CHS supports feasibility studies, probabilistic design of coastal structures, and flood risk management for coastal communities and critical infrastructure. StormSim (https://stormsim.erdc.dren.mil) is a suite of tools used for statistical analysis and probabilistic modeling of historical and synthetic storms and for stochastic design and other engineering applications. One of these tools, the Coastal Hazards Rapid Prediction System (CHRPS) (Torres et al. 2020), can perform rapid prediction of coastal storm hazards, including real-time hurricane-induced flooding. This CHETN discusses the quantification and validation of the Advanced Circulation (ADCIRC) tidal constituent database (Szpilka et al. 2016) and the tidal reconstruction program Unified Tidal analysis (UTide) (Codiga 2011) in the Puerto Rico and U.S. Virgin Islands (PR/USVI) coastal regions. The new methodology discussed herein will be further developed into the Rapid Tidal Reconstruction (RTR) tool within the StormSim and CHS frameworks.

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Bonakdarpour, Borzoo, Fuad Abujarad und Sandeep S. Kulkarni. Parallelizing Deadlock Resolution in Symbolic Synthesis of Distributed Programs. Fort Belvoir, VA: Defense Technical Information Center, Januar 2008. http://dx.doi.org/10.21236/ada487024.

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Bonakdarpour, Borzoo, und Sandeep S. Kulkarni. Exploiting Symbolic Techniques in Automated Synthesis of Distributed Programs. Fort Belvoir, VA: Defense Technical Information Center, Januar 2007. http://dx.doi.org/10.21236/ada460390.

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Abujarad, Fuad, Borzoo Bonakdarpour und Sandeep S. Kulkarni. Using Model Checking Techniques for Symbolic Synthesis of Distributed Programs. Fort Belvoir, VA: Defense Technical Information Center, Januar 2008. http://dx.doi.org/10.21236/ada487170.

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Torres, Marissa, und Norberto Nadal-Caraballo. Rapid tidal reconstruction with UTide and the ADCIRC tidal database. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41503.

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The quantification of storm surge is vital for flood hazard assessment in communities affected by coastal storms. The astronomical tide is an integral component of the total still water level needed for accurate storm surge estimates. Coastal hazard analysis methods, such as the Coastal Hazards System and the StormSim Coastal Hazards Rapid Prediction System, require thousands of hydrodynamic and wave simulations that are computationally expensive. In some regions, the inclusion of astronomical tides is neglected in the hydrodynamics and tides are instead incorporated within the probabilistic framework. There is a need for a rapid, reliable, and accurate tide prediction methodology to provide spatially dense reconstructed or predicted tidal time series for historical, synthetic, and forecasted hurricane scenarios. A methodology is proposed to combine the tidal harmonic information from the spatially dense Advanced Circulation hydrodynamic model tidal database with a rapid tidal reconstruction and prediction program. In this study, the Unified Tidal Analysis program was paired with results from the tidal database. This methodology will produce reconstructed (i.e., historical) and predicted tidal heights for coastal locations along the United States eastern seaboard and beyond and will contribute to the determination of accurate still water levels in coastal hazard analysis methods.

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Maslenikov, O. R., J. J. Johnson, L. W. Tiong, M. J. Mraz, S. Bumpus und M. A. Gerhard. SMACS: a system of computer programs for probabilistic seismic analysis of structures and subsystems. Volume I. User's manual. Office of Scientific and Technical Information (OSTI), März 1985. http://dx.doi.org/10.2172/5798909.

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Schmid, Ute, und Fritz Wysotzki. Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs. Fort Belvoir, VA: Defense Technical Information Center, Juni 2000. http://dx.doi.org/10.21236/ada382307.

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Guerin, David Christopher, Dennis L. Newell, Bruce A. Robinson, Daniel G. Levitt, Leo Van SamBeek und Gary Callahan. Salt Repository Synthesis Data of Non-Delaware Basin and International Programs for the Storage/Disposal of Nuclear Waste. Office of Scientific and Technical Information (OSTI), Oktober 2012. http://dx.doi.org/10.2172/1052768.

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Lavoie, D., N. Pinet, S. Zhang, J. Reyes, C. Jiang, O. H. Ardakani, M M Savard et al. Hudson Bay, Hudson Strait, Moose River, and Foxe basins: synthesis of the research activities under the Geomapping for Energy and Minerals (GEM) programs 2008-2018. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/314653.

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