Relevant bibliographies by topics / Syntax-Guided Synthesis

Academic literature on the topic 'Syntax-Guided Synthesis'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Syntax-Guided Synthesis"

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Morton, Kairo, William Hallahan, Elven Shum, Ruzica Piskac, and Mark Santolucito. "Grammar Filtering for Syntax-Guided Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1611–18. http://dx.doi.org/10.1609/aaai.v34i02.5522.

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Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools.
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Kumar, Mohit, Samuel Kolb, Stefano Teso, and Luc De Raedt. "Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4493–500. http://dx.doi.org/10.1609/aaai.v34i04.5877.

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Combinatorial optimization problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor in practice. The models typically consist of hard and soft constraints, or combine hard constraints with a preference function. We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. These positive and negative examples show – in a particular context – whether the solutions are good enough or not. We develop our framework using the MAX-SAT formalism. We provide learnability results within the realizable and agnostic settings, as well as hassle, an implementation based on syntax-guided synthesis and showcase its promise on recovering synthetic and benchmark instances from examples.
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Benouhiba, Toufik. "A Multi-level Refinement Approach for Structural Synthesis of Optimal Probabilistic Models." Fundamenta Informaticae 179, no. 1 (February 9, 2021): 1–33. http://dx.doi.org/10.3233/fi-2021-2011.

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Probabilistic models play an important role in many fields such as distributed systems and simulations. Like non-probabilistic systems, they can be synthesized using classical refinement-based techniques, but they also require identifying the probability distributions to be used and their parameters. Since a fully automated and blind refinement is generally undecidable, many works tried to synthesize them by looking for the parameters of the distributions. Syntax-guided synthesizing approaches are more powerful, they try to synthesize models structurally by using context-free grammars. However, many problems arise like huge search space, the complexity of generated models, and the limitation of context-free grammars to define constraints over the structure. In this paper, we propose a multi-step refinement approach, based on meta-models, offering several abstraction levels to reduce the size of the search space. More specifically, each refinement step is divided into two stages in which the desired shape of models is first described by context-sensitive constraints. In the second stage, model templates are instantiated by using global optimization techniques. We use our approach to a synthesize a set of optimal probabilistic models and show that context-sensitive constraints coupled with the multi-level abilities of the approach make the synthesis task more effective.
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Fisman, Dana, Rishabh Singh, and Armando Solar-Lezama. "Special Issue on Syntax-Guided Synthesis Preface." Formal Methods in System Design, February 28, 2022. http://dx.doi.org/10.1007/s10703-021-00386-0.

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Book chapters on the topic "Syntax-Guided Synthesis"

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Fedyukovich, Grigory, and Rastislav Bodík. "Accelerating Syntax-Guided Invariant Synthesis." In Tools and Algorithms for the Construction and Analysis of Systems, 251–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89960-2_14.

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Fedyukovich, Grigory, Sumanth Prabhu, Kumar Madhukar, and Aarti Gupta. "Quantified Invariants via Syntax-Guided Synthesis." In Computer Aided Verification, 259–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25540-4_14.

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Hu, Qinheping, Jason Breck, John Cyphert, Loris D’Antoni, and Thomas Reps. "Proving Unrealizability for Syntax-Guided Synthesis." In Computer Aided Verification, 335–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25540-4_18.

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Niemetz, Aina, Mathias Preiner, Andrew Reynolds, Clark Barrett, and Cesare Tinelli. "Syntax-Guided Quantifier Instantiation." In Tools and Algorithms for the Construction and Analysis of Systems, 145–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_8.

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AbstractThis paper presents a novel approach for quantifier instantiation in Satisfiability Modulo Theories (SMT) that leverages syntax-guided synthesis (SyGuS) to choose instantiation terms. It targets quantified constraints over background theories such as (non)linear integer, reals and floating-point arithmetic, bit-vectors, and their combinations. Unlike previous approaches for quantifier instantiation in these domains which rely on theory-specific strategies, the new approach can be applied to any (combined) theory, when provided with a grammar for instantiation terms for all sorts in the theory. We implement syntax-guided instantiation in the SMT solver CVC4, leveraging its support for enumerative SyGuS. Our experiments demonstrate the versatility of the approach, showing that it is competitive with or exceeds the performance of state-of-the-art solvers on a range of background theories.
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Hu, Qinheping, and Loris D’Antoni. "Syntax-Guided Synthesis with Quantitative Syntactic Objectives." In Computer Aided Verification, 386–403. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96145-3_21.

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Cardelli, Luca, Milan Češka, Martin Fränzle, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, and Max Whitby. "Syntax-Guided Optimal Synthesis for Chemical Reaction Networks." In Computer Aided Verification, 375–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63390-9_20.

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Polgreen, Elizabeth, Kevin Cheang, Pranav Gaddamadugu, Adwait Godbole, Kevin Laeufer, Shaokai Lin, Yatin A. Manerkar, Federico Mora, and Sanjit A. Seshia. "UCLID5: Multi-modal Formal Modeling, Verification, and Synthesis." In Computer Aided Verification, 538–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_27.

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AbstractUCLID5 is a tool for the multi-modal formal modeling, verification, and synthesis of systems. It enables one to tackle verification problems for heterogeneous systems such as combinations of hardware and software, or those that have multiple, varied specifications, or systems that require hybrid modes of modeling. A novel aspect of UCLID5 is an emphasis on the use of syntax-guided and inductive synthesis to automate steps in modeling and verification. This tool paper presents new developments in the UCLID5 tool including new language features, integration with new techniques for syntax-guided synthesis and satisfiability solving, support for hyperproperties and combinations of axiomatic and operational modeling, demonstrations on new problem classes, and a robust implementation.
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Reynolds, Andrew, Haniel Barbosa, Daniel Larraz, and Cesare Tinelli. "Scalable Algorithms for Abduction via Enumerative Syntax-Guided Synthesis." In Automated Reasoning, 141–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51074-9_9.

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Reynolds, Andrew, Haniel Barbosa, Andres Nötzli, Clark Barrett, and Cesare Tinelli. "cvc4sy: Smart and Fast Term Enumeration for Syntax-Guided Synthesis." In Computer Aided Verification, 74–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25543-5_5.

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Zhang, Hongce, Aarti Gupta, and Sharad Malik. "Syntax-Guided Synthesis for Lemma Generation in Hardware Model Checking." In Lecture Notes in Computer Science, 325–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67067-2_15.

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Conference papers on the topic "Syntax-Guided Synthesis"

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Alur, Rajeev, Rastislav Bodik, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa. "Syntax-guided synthesis." In 2013 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2013. http://dx.doi.org/10.1109/fmcad.2013.6679385.

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Si, Xujie, Woosuk Lee, Richard Zhang, Aws Albarghouthi, Paraschos Koutris, and Mayur Naik. "Syntax-guided synthesis of Datalog programs." In ESEC/FSE '18: 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3236024.3236034.

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Alur, Rajeev. "Keynote talk I: Syntax-guided synthesis." In 2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE). IEEE, 2015. http://dx.doi.org/10.1109/memcod.2015.7340460.

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Choi, Wonhyuk. "Can reactive synthesis and syntax-guided synthesis be friends?" In SPLASH '21: Software for Humanity. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3484271.3484972.

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Choi, Wonhyuk, Bernd Finkbeiner, Ruzica Piskac, and Mark Santolucito. "Can reactive synthesis and syntax-guided synthesis be friends?" In PLDI '22: 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3519939.3523429.

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Wu, Zhengkai, Vu Le, Ashish Tiwari, Sumit Gulwani, Arjun Radhakrishna, Ivan Radiček, Gustavo Soares, Xinyu Wang, Zhenwen Li, and Tao Xie. "NL2Viz: natural language to visualization via constrained syntax-guided synthesis." In ESEC/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3540250.3549140.

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Le, Xuan-Bach D., Duc-Hiep Chu, David Lo, Claire Le Goues, and Willem Visser. "S3: syntax- and semantic-guided repair synthesis via programming by examples." In ESEC/FSE'17: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106237.3106309.

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Golia, Priyanka, Subhajit Roy, and Kuldeep S. Meel. "Program Synthesis as Dependency Quantified Formula Modulo Theory." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/261.

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Given a specification φ(X, Y ) over inputs X and output Y and defined over a background theory T, the problem of program synthesis is to design a program f such that Y = f (X), satisfies the specification φ. Over the past decade, syntax-guided synthesis (SyGuS) has emerged as a dominant approach to program synthesis where in addition to the specification φ, the end-user also specifies a grammar L to aid the underlying synthesis engine. This paper investigates the feasibility of synthesis techniques without grammar, a sub-class defined as T constrained synthesis. We show that T-constrained synthesis can be reduced to DQF(T),i.e., to the problem of finding a witness of a dependency quantified formula modulo theory. When the underlying theory is the theory of bitvectors, the corresponding DQF problem can be further reduced to Dependency Quantified Boolean Formulas (DQBF). We rely on the progress in DQBF solving to design DQBF-based synthesizers that outperform the domain-specific program synthesis techniques; thereby positioning DQBF as a core representation language for program synthesis. Our empirical analysis shows that T-constrained synthesis can achieve significantly better performance than syntax-guided approaches. Furthermore, the general-purpose DQBF solvers perform on par with domain-specific synthesis techniques.
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Hu, Qinheping, John Cyphert, Loris D'Antoni, and Thomas Reps. "Exact and approximate methods for proving unrealizability of syntax-guided synthesis problems." In PLDI '20: 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3385412.3385979.

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