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Journal articles on the topic 'Synthesis of Probabilistic Programs'

Author: Grafiati

Published: 28 June 2021

Last updated: 2 February 2022

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1

Nori, Aditya V., Sherjil Ozair, Sriram K. Rajamani, and Deepak Vijaykeerthy. "Efficient synthesis of probabilistic programs." ACM SIGPLAN Notices 50, no. 6 (August 7, 2015): 208–17. http://dx.doi.org/10.1145/2813885.2737982.

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Salustowicz, Rafal, and Jürgen Schmidhuber. "Probabilistic Incremental Program Evolution." Evolutionary Computation 5, no. 2 (June 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, and Vikash K. Mansinghka. "Bayesian synthesis of probabilistic programs for automatic data modeling." Proceedings of the ACM on Programming Languages 3, POPL (January 2, 2019): 1–32. http://dx.doi.org/10.1145/3290350.

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4

Satake, Yuki, Hiroshi Unno, and Hinata Yanagi. "Probabilistic Inference for Predicate Constraint Satisfaction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 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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5

Lee, Woosuk, Kihong Heo, Rajeev Alur, and Mayur Naik. "Accelerating search-based program synthesis using learned probabilistic models." ACM SIGPLAN Notices 53, no. 4 (December 2, 2018): 436–49. http://dx.doi.org/10.1145/3296979.3192410.

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6

Kemper, C. A., N. M. Lane, R. W. Carlson, M. A. Musen, and S. W. Tu. "A Methodology for Determining Patients’ Eligibility for Clinical Trials." Methods of Information in Medicine 32, no. 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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7

Chakraborty, Sourav, and Kuldeep S. Meel. "On Testing of Uniform Samplers." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 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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8

Dekhtyar, Alex, and V. S. Subrahmanian. "Hybrid probabilistic programs." Journal of Logic Programming 43, no. 3 (June 2000): 187–250. http://dx.doi.org/10.1016/s0743-1066(99)00059-x.

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9

Dix, Jürgen, Mirco Nanni, and V. S. Subrahmanian. "Probabilistic agent programs." ACM Transactions on Computational Logic 1, no. 2 (October 2000): 208–46. http://dx.doi.org/10.1145/359496.359508.

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10

Hur, Chung-Kil, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel. "Slicing probabilistic programs." ACM SIGPLAN Notices 49, no. 6 (June 5, 2014): 133–44. http://dx.doi.org/10.1145/2666356.2594303.

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11

Kuznetsov, Sergey. "The innovative technologists and formation of the technical operation system of civil aircraft avionics." MATEC Web of Conferences 341 (2021): 00049. http://dx.doi.org/10.1051/matecconf/202134100049.

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The system of technical operation (TO) of on-board equipment or avionics of aircraft (AC) as an object of research is a set of objects and means of technical operation, maintenance and repair programs, as well as personnel performing procedures and organizing the processes of TO. The quality of the TO system is manifested in the TO process - a set of processes of intended use, operational control, maintenance, restoration and repair. The TO process, as a process of changing the TO states, can be considered as a controlled random process, determined on the set of operation states by probabilistic characteristics. The quality of the TO system is characterized by a set of properties that determine its ability to satisfy with the maximum economic efficiency the needs of the TO system of the aircraft, while ensuring the required levels of reliability and readiness of the avionics for operation. That is, the TO system has all the features inherent in complex technical systems - a hierarchical ramified structure, subordination of goals and restrictions, wide interrelationships in the process of functioning. The main research apparatus in solving problems of optimization of processes and synthesis of TO systems is the apparatus of the theory of mathematical modeling of complex technical systems, based on a system approach, the integration of analytical and simulation modeling. The article solves the problem of determining the values of the parameters of the TO system such that the costs of the system during the TO process reach a minimum when all the required tasks are completed and all restrictions on the system’s own parameters and indicators of its technical efficiency are met.

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12

De Raedt, L., K. Kersting, A. Kimmig, K. Revoredo, and H. Toivonen. "Compressing probabilistic Prolog programs." Machine Learning 70, no. 2-3 (November 8, 2007): 151–68. http://dx.doi.org/10.1007/s10994-007-5030-x.

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13

Lukasiewicz, Thomas. "Probabilistic description logic programs." International Journal of Approximate Reasoning 45, no. 2 (July 2007): 288–307. http://dx.doi.org/10.1016/j.ijar.2006.06.012.

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14

Chadha, R., L. Cruz-Filipe, P. Mateus, and A. Sernadas. "Reasoning about probabilistic sequential programs." Theoretical Computer Science 379, no. 1-2 (June 2007): 142–65. http://dx.doi.org/10.1016/j.tcs.2007.02.040.

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15

Sankaranarayanan, Sriram, Aleksandar Chakarov, and Sumit Gulwani. "Static analysis for probabilistic programs." ACM SIGPLAN Notices 48, no. 6 (June 23, 2013): 447–58. http://dx.doi.org/10.1145/2499370.2462179.

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16

Cusumano-Towner, Marco, Benjamin Bichsel, Timon Gehr, Martin Vechev, and Vikash K. Mansinghka. "Incremental inference for probabilistic programs." ACM SIGPLAN Notices 53, no. 4 (December 2, 2018): 571–85. http://dx.doi.org/10.1145/3296979.3192399.

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17

Rao, Josyula R. "Reasoning about probabilistic parallel programs." ACM Transactions on Programming Languages and Systems 16, no. 3 (May 1994): 798–842. http://dx.doi.org/10.1145/177492.177724.

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18

Chen, Yixiang, and Hengyang Wu. "Semantics of sub-probabilistic programs." Frontiers of Computer Science in China 2, no. 1 (March 2008): 29–38. http://dx.doi.org/10.1007/s11704-008-0004-0.

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19

Meinicke, Larissa, and Kim Solin. "Refinement algebra for probabilistic programs." Formal Aspects of Computing 22, no. 1 (April 17, 2009): 3–31. http://dx.doi.org/10.1007/s00165-009-0111-1.

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20

Kaz'mina, E. A. "Probabilistic semantics of terminating programs." USSR Computational Mathematics and Mathematical Physics 28, no. 2 (January 1988): 82–88. http://dx.doi.org/10.1016/0041-5553(88)90146-2.

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21

Meinicke, Larissa, and Kim Solin. "Refinement Algebra for Probabilistic Programs." Electronic Notes in Theoretical Computer Science 201 (March 2008): 177–95. http://dx.doi.org/10.1016/j.entcs.2008.02.020.

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22

Ying, M. "Reasoning about probabilistic sequential programs in a probabilistic logic." Acta Informatica 39, no. 5 (May 1, 2003): 315–89. http://dx.doi.org/10.1007/s00236-003-0113-z.

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23

Salehi, Khayyam, Jaber Karimpour, Habib Izadkhah, and Ayaz Isazadeh. "Channel Capacity of Concurrent Probabilistic Programs." Entropy 21, no. 9 (September 12, 2019): 885. http://dx.doi.org/10.3390/e21090885.

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Programs are under continuous attack for disclosing secret information, and defending against these attacks is becoming increasingly vital. An attractive approach for protection is to measure the amount of secret information that might leak to attackers. A fundamental issue in computing information leakage is that given a program and attackers with various knowledge of the secret information, what is the maximum amount of leakage of the program? This is called channel capacity. In this paper, two notions of capacity are defined for concurrent probabilistic programs using information theory. These definitions consider intermediate leakage and the scheduler effect. These capacities are computed by a constrained nonlinear optimization problem. Therefore, an evolutionary algorithm is proposed to compute the capacities. Single preference voting and dining cryptographers protocols are analyzed as case studies to show how the proposed approach can automatically compute the capacities. The results demonstrate that there are attackers who can learn the whole secret of both the single preference protocol and dining cryptographers protocol. The proposed evolutionary algorithm is a general approach for computing any type of capacity in any kind of program.

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24

McIver, A. K., and Carroll Morgan. "Partial correctness for probabilistic demonic programs." Theoretical Computer Science 266, no. 1-2 (September 2001): 513–41. http://dx.doi.org/10.1016/s0304-3975(00)00208-5.

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25

Martynov, G. V. "Probabilistic-statistical programs from ?applied statistics?" Journal of Soviet Mathematics 50, no. 3 (June 1990): 1643–84. http://dx.doi.org/10.1007/bf01096290.

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26

Barthe, Gilles, Benjamin Grégoire, Justin Hsu, and Pierre-Yves Strub. "Coupling proofs are probabilistic product programs." ACM SIGPLAN Notices 52, no. 1 (May 11, 2017): 161–74. http://dx.doi.org/10.1145/3093333.3009896.

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27

Barthe, Gilles, Thomas Espitau, Benjamin Grégoire, Justin Hsu, and Pierre-Yves Strub. "Proving expected sensitivity of probabilistic programs." Proceedings of the ACM on Programming Languages 2, POPL (January 2018): 1–29. http://dx.doi.org/10.1145/3158145.

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28

Buchman, David, and David Poole. "Negative probabilities in probabilistic logic programs." International Journal of Approximate Reasoning 83 (April 2017): 43–59. http://dx.doi.org/10.1016/j.ijar.2016.10.001.

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29

Szabo, M. E., and E. J. Farkas. "A probabilistic analysis of loop programs." Computer Languages 14, no. 2 (January 1989): 125–36. http://dx.doi.org/10.1016/0096-0551(89)90019-2.

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30

Mironov, A. M., and S. L. Frenkel. "Minimization of Probabilistic Models of Programs." Journal of Mathematical Sciences 211, no. 3 (October 19, 2015): 381–412. http://dx.doi.org/10.1007/s10958-015-2611-2.

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31

Kirkeby, Maja Hanne. "Probabilistic Output Analyses for Deterministic Programs — Reusing Existing Non-probabilistic Analyses." Electronic Proceedings in Theoretical Computer Science 312 (January 20, 2020): 43–57. http://dx.doi.org/10.4204/eptcs.312.4.

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32

Hsiung, Chris C. S., Arjun Raj, and Gerd A. Blobel. "Hematopoietic Transcriptional Regulation At The Mitosis-G1 Transition." Blood 122, no. 21 (November 15, 2013): 2440. http://dx.doi.org/10.1182/blood.v122.21.2440.2440.

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Abstract Normal hematopoiesis involves the coordination of cell division and gene expression to produce physiologically appropriate cell numbers of various developmental stages across lineages. While studies have demonstrated intricate links between cell cycle progression and developmental gene regulation -- two cellular programs whose concomitant dysregulation is central to many malignant and non-malignant hematologic diseases -- researchers currently lack clear, general principles of how intrinsic properties of cell division could influence developmental gene regulation. In each round of division, mitosis imposes a striking disruption to gene expression: the nucleus is disassembled, bulk RNA synthesis ceases, and the transcription machinery and most transcription factors -- including repressive complexes -- are evicted from mitotic chromatin. Since hematopoietic lineage fidelity often requires the continued presence of repressive complexes to inhibit expression of developmentally inappropriate genes, we hypothesized that such repression may be inefficient during a narrow window immediately post-mitosis, resulting in transient aberrant transcription in a probabilistic manner. We tested for the presence of transient post-mitotic aberrant transcription at genes whose repression is known to depend on continued occupancy of repressive complexes. We used an experimentally tractable cell line, G1E cells, a rapidly dividing model of lineage-committed murine pro-erythroblasts that genetically lack the erythroid master regulator Gata1. Transduction with a Gata1-estrogen receptor fusion construct and treatment with estradiol restores Gata1 function, leading to recapitulation of early erythroid maturation events, including rapid repression of stemness-associated genes, such as Gata2 and c-Kit. We examined in fine temporal detail the post-mitotic transcriptional behavior of Gata2, c-Kit and other genes using population-based assays facilitated by drug-mediated cell cycle synchronization. In addition, we bypassed the use of synchronization drugs and their associated potential experimental artifacts by developing novel complementary methods to study the relationship between cell cycle status and transcription in asynchronous populations: 1. We harnessed single-molecule RNA fluorescence in situ hybridization technology to quantitatively assess transcription in individual cells at various cell cycle stages, and 2. We adapted a fluorescent protein cell cycle reporter to separate, using fluorescence-activated cell sorting, subpopulations of specific cell cycle stages for epigenomic and transcriptomic analyses. Together, our results revealed a post-mitotic pulse of increased RNA polymerase II recruitment and transcript synthesis most clearly exhibited by Gata2, c-Kit, and other genes whose repression is known to depend on co-repressor complexes in these cells. Our results support the notion that the mitosis-G1 transition presents a window of transcriptional plasticity. We are beginning to explore how this property of post-mitotic transcriptional control applies to hematopoietic cell types across the developmental spectrum and could contribute to functionally important variations in gene expression, such as in stem cell lineage commitment, experimental reprogramming, and non-genetic heterogeneity in malignancy. Disclosures: No relevant conflicts of interest to declare.

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33

DEN HARTOG, J. I., and E. P. DE VINK. "VERIFYING PROBABILISTIC PROGRAMS USING A HOARE LIKE LOGIC." International Journal of Foundations of Computer Science 13, no. 03 (June 2002): 315–40. http://dx.doi.org/10.1142/s012905410200114x.

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Probability, be it inherent or explicitly introduced, has become an important issue in the verification of programs. In this paper we study a formalism which allows reasoning about programs which can act probabilistically. To describe probabilistic programs, a basic programming language with an operator for probabilistic choice is introduced and a denotational semantics is given for this language. To specify propertics of probabilistic programs, standard first order logic predicates are insufficient, so a notion of probabilistic predicates is introduced. A Hoare-style proof system to check properties of probabilistic programs is given. The proof system for a sublanguage is shown to be sound and complete; the properties that can be derived are exactly the valid properties. Finally some typical examples illustrate the use of the probabilistic predicates and the proof system.

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34

Dieu, Phan Dinh, and Phan Hong Giang. "Interval –valued probabilistic logic for logic programs." Journal of Computer Science and Cybernetics 10, no. 3 (April 15, 2016): 1–13. http://dx.doi.org/10.15625/1813-9663/10/3/8193.

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This paper presents an approximate method for probabilistic entailment problem in knowledge bases where a portion of knowledge is given by a sentence in propositional logic accompanied with an interval presenting its truth probalibity. This method reduces the entailment problem to one of finding “prime implicants” of the target sentence expressed through sentences in the given knowledge base. It is shown that in the case of probabilistic logic programs the set of such prime implicants can be found by using the SLD-resolution method for usual definte logic programs.

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35

Ghosh, Sarthak, and C. R. Ramakrishnan. "Value of Information in Probabilistic Logic Programs." Electronic Proceedings in Theoretical Computer Science 306 (September 19, 2019): 71–84. http://dx.doi.org/10.4204/eptcs.306.14.

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36

Holtzen, Steven, Guy Van den Broeck, and Todd Millstein. "Scaling exact inference for discrete probabilistic programs." Proceedings of the ACM on Programming Languages 4, OOPSLA (November 13, 2020): 1–31. http://dx.doi.org/10.1145/3428208.

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37

Avanzini, Martin, Georg Moser, and Michael Schaper. "A modular cost analysis for probabilistic programs." Proceedings of the ACM on Programming Languages 4, OOPSLA (November 13, 2020): 1–30. http://dx.doi.org/10.1145/3428240.

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38

Dräger, Klaus, Marta Kwiatkowska, David Parker, and Hongyang Qu. "Local abstraction refinement for probabilistic timed programs." Theoretical Computer Science 538 (June 2014): 37–53. http://dx.doi.org/10.1016/j.tcs.2013.07.013.

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39

NAMPALLY, ARUN, TIMOTHY ZHANG, and C. R. RAMAKRISHNAN. "Constraint-Based Inference in Probabilistic Logic Programs." Theory and Practice of Logic Programming 18, no. 3-4 (July 2018): 638–55. http://dx.doi.org/10.1017/s1471068418000273.

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AbstractProbabilistic Logic Programs (PLPs) generalize traditional logic programs and allow the encoding of models combining logical structure and uncertainty. In PLP, inference is performed by summarizing the possible worlds which entail the query in a suitable data structure, and using this data structure to compute the answer probability. Systems such as ProbLog, PITA, etc., use propositional data structures like explanation graphs, BDDs, SDDs, etc., to represent the possible worlds. While this approach saves inference time due to substructure sharing, there are a number of problems where a more compact data structure is possible. We propose a data structure called Ordered Symbolic Derivation Diagram (OSDD) which captures the possible worlds by means of constraint formulas. We describe a program transformation technique to construct OSDDs via query evaluation, and give procedures to perform exact and approximate inference over OSDDs. Our approach has two key properties. Firstly, the exact inference procedure is a generalization of traditional inference, and results in speedup over the latter in certain settings. Secondly, the approximate technique is a generalization of likelihood weighting in Bayesian Networks, and allows us to perform sampling-based inference with lower rejection rate and variance. We evaluate the effectiveness of the proposed techniques through experiments on several problems.

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40

Franke, Björn, Michael O'Boyle, John Thomson, and Grigori Fursin. "Probabilistic source-level optimisation of embedded programs." ACM SIGPLAN Notices 40, no. 7 (July 12, 2005): 78–86. http://dx.doi.org/10.1145/1070891.1065922.

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41

Narayanan, Praveen, and Chung-chieh Shan. "Symbolic conditioning of arrays in probabilistic programs." Proceedings of the ACM on Programming Languages 1, ICFP (August 29, 2017): 1–25. http://dx.doi.org/10.1145/3110255.

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42

Riguzzi, F. "Speeding Up Inference for Probabilistic Logic Programs." Computer Journal 57, no. 3 (August 26, 2013): 347–63. http://dx.doi.org/10.1093/comjnl/bxt096.

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43

Ngo, Van Chan, Quentin Carbonneaux, and Jan Hoffmann. "Bounded expectations: resource analysis for probabilistic programs." ACM SIGPLAN Notices 53, no. 4 (December 2, 2018): 496–512. http://dx.doi.org/10.1145/3296979.3192394.

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44

Branda, Martin, and Jitka Dupačová. "Approximation and contamination bounds for probabilistic programs." Annals of Operations Research 193, no. 1 (November 11, 2010): 3–19. http://dx.doi.org/10.1007/s10479-010-0811-1.

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45

Dekhtyar, Alex, and Michael I. Dekhtyar. "The theory of interval probabilistic logic programs." Annals of Mathematics and Artificial Intelligence 55, no. 3-4 (December 19, 2008): 355–88. http://dx.doi.org/10.1007/s10472-008-9104-7.

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46

Nguembang Fadja, Arnaud, and Fabrizio Riguzzi. "Lifted discriminative learning of probabilistic logic programs." Machine Learning 108, no. 7 (August 20, 2018): 1111–35. http://dx.doi.org/10.1007/s10994-018-5750-0.

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47

Kaminski, Benjamin Lucien, Joost-Pieter Katoen, and Christoph Matheja. "On the hardness of analyzing probabilistic programs." Acta Informatica 56, no. 3 (May 15, 2018): 255–85. http://dx.doi.org/10.1007/s00236-018-0321-1.

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48

BARAL, CHITTA, MICHAEL GELFOND, and NELSON RUSHTON. "Probabilistic reasoning with answer sets." Theory and Practice of Logic Programming 9, no. 1 (January 2009): 57–144. http://dx.doi.org/10.1017/s1471068408003645.

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AbstractThis paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs.

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49

Agrawal, M., and T. Thierauf. "The Satisfiability Problem for Probabilistic Ordered Branching Programs." Theory of Computing Systems 34, no. 5 (October 2001): 471–87. http://dx.doi.org/10.1007/s00224-001-1011-9.

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50

Simari, Gerardo I., John P. Dickerson, Amy Sliva, and V. S. Subrahmanian. "Parallel Abductive Query Answering in Probabilistic Logic Programs." ACM Transactions on Computational Logic 14, no. 2 (June 2013): 1–39. http://dx.doi.org/10.1145/2480759.2480764.

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