@inproceedings{8571, abstract = {We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020. This year, 6 tools Ariadne, CORA, DynIbex, Flow*, Isabelle/HOL, and JuliaReach (in alphabetic order) participated. These tools are applied to solve reachability analysis problems on six benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools.}, author = {Geretti, Luca and Alexandre Dit Sandretto, Julien and Althoff, Matthias and Benet, Luis and Chapoutot, Alexandre and Chen, Xin and Collins, Pieter and Forets, Marcelo and Freire, Daniel and Immler, Fabian and Kochdumper, Niklas and Sanders, David and Schilling, Christian}, booktitle = {EPiC Series in Computing}, pages = {49--75}, publisher = {EasyChair}, title = {{ARCH-COMP20 Category Report: Continuous and hybrid systems with nonlinear dynamics}}, doi = {10.29007/zkf6}, volume = {74}, year = {2020}, } @inproceedings{8600, abstract = {A vector addition system with states (VASS) consists of a finite set of states and counters. A transition changes the current state to the next state, and every counter is either incremented, or decremented, or left unchanged. A state and value for each counter is a configuration; and a computation is an infinite sequence of configurations with transitions between successive configurations. A probabilistic VASS consists of a VASS along with a probability distribution over the transitions for each state. Qualitative properties such as state and configuration reachability have been widely studied for VASS. In this work we consider multi-dimensional long-run average objectives for VASS and probabilistic VASS. For a counter, the cost of a configuration is the value of the counter; and the long-run average value of a computation for the counter is the long-run average of the costs of the configurations in the computation. The multi-dimensional long-run average problem given a VASS and a threshold value for each counter, asks whether there is a computation such that for each counter the long-run average value for the counter does not exceed the respective threshold. For probabilistic VASS, instead of the existence of a computation, we consider whether the expected long-run average value for each counter does not exceed the respective threshold. Our main results are as follows: we show that the multi-dimensional long-run average problem (a) is NP-complete for integer-valued VASS; (b) is undecidable for natural-valued VASS (i.e., nonnegative counters); and (c) can be solved in polynomial time for probabilistic integer-valued VASS, and probabilistic natural-valued VASS when all computations are non-terminating.}, author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan}, booktitle = {31st International Conference on Concurrency Theory}, isbn = {9783959771603}, issn = {18688969}, location = {Virtual}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Multi-dimensional long-run average problems for vector addition systems with states}}, doi = {10.4230/LIPIcs.CONCUR.2020.23}, volume = {171}, year = {2020}, } @inproceedings{8599, abstract = {A graph game is a two-player zero-sum game in which the players move a token throughout a graph to produce an infinite path, which determines the winner or payoff of the game. In bidding games, both players have budgets, and in each turn, we hold an "auction" (bidding) to determine which player moves the token. In this survey, we consider several bidding mechanisms and study their effect on the properties of the game. Specifically, bidding games, and in particular bidding games of infinite duration, have an intriguing equivalence with random-turn games in which in each turn, the player who moves is chosen randomly. We show how minor changes in the bidding mechanism lead to unexpected differences in the equivalence with random-turn games.}, author = {Avni, Guy and Henzinger, Thomas A}, booktitle = {31st International Conference on Concurrency Theory}, isbn = {9783959771603}, issn = {18688969}, location = {Virtual}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{A survey of bidding games on graphs}}, doi = {10.4230/LIPIcs.CONCUR.2020.2}, volume = {171}, year = {2020}, } @inproceedings{9040, abstract = {Machine learning and formal methods have complimentary benefits and drawbacks. In this work, we address the controller-design problem with a combination of techniques from both fields. The use of black-box neural networks in deep reinforcement learning (deep RL) poses a challenge for such a combination. Instead of reasoning formally about the output of deep RL, which we call the wizard, we extract from it a decision-tree based model, which we refer to as the magic book. Using the extracted model as an intermediary, we are able to handle problems that are infeasible for either deep RL or formal methods by themselves. First, we suggest, for the first time, a synthesis procedure that is based on a magic book. We synthesize a stand-alone correct-by-design controller that enjoys the favorable performance of RL. Second, we incorporate a magic book in a bounded model checking (BMC) procedure. BMC allows us to find numerous traces of the plant under the control of the wizard, which a user can use to increase the trustworthiness of the wizard and direct further training.}, author = {Alamdari, Par Alizadeh and Avni, Guy and Henzinger, Thomas A and Lukina, Anna}, booktitle = {Proceedings of the 20th Conference on Formal Methods in Computer-Aided Design}, isbn = {9783854480426}, issn = {2708-7824}, location = {Online Conference}, pages = {138--147}, publisher = {TU Wien Academic Press}, title = {{Formal methods with a touch of magic}}, doi = {10.34727/2020/isbn.978-3-85448-042-6_21}, year = {2020}, } @inproceedings{9632, abstract = {Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep neural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as illustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher.}, author = {Singh, Sidak Pal and Alistarh, Dan-Adrian}, booktitle = {Advances in Neural Information Processing Systems}, isbn = {9781713829546}, issn = {10495258}, location = {Vancouver, Canada}, pages = {18098--18109}, publisher = {Curran Associates}, title = {{WoodFisher: Efficient second-order approximation for neural network compression}}, volume = {33}, year = {2020}, } @inproceedings{9103, abstract = {We introduce LRT-NG, a set of techniques and an associated toolset that computes a reachtube (an over-approximation of the set of reachable states over a given time horizon) of a nonlinear dynamical system. LRT-NG significantly advances the state-of-the-art Langrangian Reachability and its associated tool LRT. From a theoretical perspective, LRT-NG is superior to LRT in three ways. First, it uses for the first time an analytically computed metric for the propagated ball which is proven to minimize the ball’s volume. We emphasize that the metric computation is the centerpiece of all bloating-based techniques. Secondly, it computes the next reachset as the intersection of two balls: one based on the Cartesian metric and the other on the new metric. While the two metrics were previously considered opposing approaches, their joint use considerably tightens the reachtubes. Thirdly, it avoids the "wrapping effect" associated with the validated integration of the center of the reachset, by optimally absorbing the interval approximation in the radius of the next ball. From a tool-development perspective, LRT-NG is superior to LRT in two ways. First, it is a standalone tool that no longer relies on CAPD. This required the implementation of the Lohner method and a Runge-Kutta time-propagation method. Secondly, it has an improved interface, allowing the input model and initial conditions to be provided as external input files. Our experiments on a comprehensive set of benchmarks, including two Neural ODEs, demonstrates LRT-NG’s superior performance compared to LRT, CAPD, and Flow*.}, author = {Gruenbacher, Sophie and Cyranka, Jacek and Lechner, Mathias and Islam, Md Ariful and Smolka, Scott A. and Grosu, Radu}, booktitle = {Proceedings of the 59th IEEE Conference on Decision and Control}, isbn = {9781728174471}, issn = {07431546}, location = {Jeju Islang, Korea (South)}, pages = {1556--1563}, publisher = {IEEE}, title = {{Lagrangian reachtubes: The next generation}}, doi = {10.1109/CDC42340.2020.9304042}, volume = {2020}, year = {2020}, } @inproceedings{10672, abstract = {The family of feedback alignment (FA) algorithms aims to provide a more biologically motivated alternative to backpropagation (BP), by substituting the computations that are unrealistic to be implemented in physical brains. While FA algorithms have been shown to work well in practice, there is a lack of rigorous theory proofing their learning capabilities. Here we introduce the first feedback alignment algorithm with provable learning guarantees. In contrast to existing work, we do not require any assumption about the size or depth of the network except that it has a single output neuron, i.e., such as for binary classification tasks. We show that our FA algorithm can deliver its theoretical promises in practice, surpassing the learning performance of existing FA methods and matching backpropagation in binary classification tasks. Finally, we demonstrate the limits of our FA variant when the number of output neurons grows beyond a certain quantity.}, author = {Lechner, Mathias}, booktitle = {8th International Conference on Learning Representations}, location = {Virtual ; Addis Ababa, Ethiopia}, publisher = {ICLR}, title = {{Learning representations for binary-classification without backpropagation}}, year = {2020}, } @inproceedings{7808, abstract = {Quantization converts neural networks into low-bit fixed-point computations which can be carried out by efficient integer-only hardware, and is standard practice for the deployment of neural networks on real-time embedded devices. However, like their real-numbered counterpart, quantized networks are not immune to malicious misclassification caused by adversarial attacks. We investigate how quantization affects a network’s robustness to adversarial attacks, which is a formal verification question. We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. We built a tool and analyzed the effect of quantization on a classifier for the MNIST dataset. We demonstrate that, compared to our method, existing methods for the analysis of real-numbered networks often derive false conclusions about their quantizations, both when determining robustness and when detecting attacks, and that existing methods for quantized networks often miss attacks. Furthermore, we applied our method beyond robustness, showing how the number of bits in quantization enlarges the gender bias of a predictor for students’ grades.}, author = {Giacobbe, Mirco and Henzinger, Thomas A and Lechner, Mathias}, booktitle = {International Conference on Tools and Algorithms for the Construction and Analysis of Systems}, isbn = {9783030452360}, issn = {16113349}, location = {Dublin, Ireland}, pages = {79--97}, publisher = {Springer Nature}, title = {{How many bits does it take to quantize your neural network?}}, doi = {10.1007/978-3-030-45237-7_5}, volume = {12079}, year = {2020}, } @article{6761, abstract = {In resource allocation games, selfish players share resources that are needed in order to fulfill their objectives. The cost of using a resource depends on the load on it. In the traditional setting, the players make their choices concurrently and in one-shot. That is, a strategy for a player is a subset of the resources. We introduce and study dynamic resource allocation games. In this setting, the game proceeds in phases. In each phase each player chooses one resource. A scheduler dictates the order in which the players proceed in a phase, possibly scheduling several players to proceed concurrently. The game ends when each player has collected a set of resources that fulfills his objective. The cost for each player then depends on this set as well as on the load on the resources in it – we consider both congestion and cost-sharing games. We argue that the dynamic setting is the suitable setting for many applications in practice. We study the stability of dynamic resource allocation games, where the appropriate notion of stability is that of subgame perfect equilibrium, study the inefficiency incurred due to selfish behavior, and also study problems that are particular to the dynamic setting, like constraints on the order in which resources can be chosen or the problem of finding a scheduler that achieves stability.}, author = {Avni, Guy and Henzinger, Thomas A and Kupferman, Orna}, issn = {03043975}, journal = {Theoretical Computer Science}, pages = {42--55}, publisher = {Elsevier}, title = {{Dynamic resource allocation games}}, doi = {10.1016/j.tcs.2019.06.031}, volume = {807}, year = {2020}, } @inproceedings{7505, abstract = {Neural networks have demonstrated unmatched performance in a range of classification tasks. Despite numerous efforts of the research community, novelty detection remains one of the significant limitations of neural networks. The ability to identify previously unseen inputs as novel is crucial for our understanding of the decisions made by neural networks. At runtime, inputs not falling into any of the categories learned during training cannot be classified correctly by the neural network. Existing approaches treat the neural network as a black box and try to detect novel inputs based on the confidence of the output predictions. However, neural networks are not trained to reduce their confidence for novel inputs, which limits the effectiveness of these approaches. We propose a framework to monitor a neural network by observing the hidden layers. We employ a common abstraction from program analysis - boxes - to identify novel behaviors in the monitored layers, i.e., inputs that cause behaviors outside the box. For each neuron, the boxes range over the values seen in training. The framework is efficient and flexible to achieve a desired trade-off between raising false warnings and detecting novel inputs. We illustrate the performance and the robustness to variability in the unknown classes on popular image-classification benchmarks.}, author = {Henzinger, Thomas A and Lukina, Anna and Schilling, Christian}, booktitle = {24th European Conference on Artificial Intelligence}, location = {Santiago de Compostela, Spain}, pages = {2433--2440}, publisher = {IOS Press}, title = {{Outside the box: Abstraction-based monitoring of neural networks}}, doi = {10.3233/FAIA200375}, volume = {325}, year = {2020}, } @inproceedings{8194, abstract = {Fixed-point arithmetic is a popular alternative to floating-point arithmetic on embedded systems. Existing work on the verification of fixed-point programs relies on custom formalizations of fixed-point arithmetic, which makes it hard to compare the described techniques or reuse the implementations. In this paper, we address this issue by proposing and formalizing an SMT theory of fixed-point arithmetic. We present an intuitive yet comprehensive syntax of the fixed-point theory, and provide formal semantics for it based on rational arithmetic. We also describe two decision procedures for this theory: one based on the theory of bit-vectors and the other on the theory of reals. We implement the two decision procedures, and evaluate our implementations using existing mature SMT solvers on a benchmark suite we created. Finally, we perform a case study of using the theory we propose to verify properties of quantized neural networks.}, author = {Baranowski, Marek and He, Shaobo and Lechner, Mathias and Nguyen, Thanh Son and Rakamarić, Zvonimir}, booktitle = {Automated Reasoning}, isbn = {9783030510732}, issn = {16113349}, location = {Paris, France}, pages = {13--31}, publisher = {Springer Nature}, title = {{An SMT theory of fixed-point arithmetic}}, doi = {10.1007/978-3-030-51074-9_2}, volume = {12166}, year = {2020}, } @article{8679, abstract = {A central goal of artificial intelligence in high-stakes decision-making applications is to design a single algorithm that simultaneously expresses generalizability by learning coherent representations of their world and interpretable explanations of its dynamics. Here, we combine brain-inspired neural computation principles and scalable deep learning architectures to design compact neural controllers for task-specific compartments of a full-stack autonomous vehicle control system. We discover that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learns to map high-dimensional inputs into steering commands. This system shows superior generalizability, interpretability and robustness compared with orders-of-magnitude larger black-box learning systems. The obtained neural agents enable high-fidelity autonomy for task-specific parts of a complex autonomous system.}, author = {Lechner, Mathias and Hasani, Ramin and Amini, Alexander and Henzinger, Thomas A and Rus, Daniela and Grosu, Radu}, issn = {2522-5839}, journal = {Nature Machine Intelligence}, pages = {642--652}, publisher = {Springer Nature}, title = {{Neural circuit policies enabling auditable autonomy}}, doi = {10.1038/s42256-020-00237-3}, volume = {2}, year = {2020}, } @inproceedings{8704, abstract = {Traditional robotic control suits require profound task-specific knowledge for designing, building and testing control software. The rise of Deep Learning has enabled end-to-end solutions to be learned entirely from data, requiring minimal knowledge about the application area. We design a learning scheme to train end-to-end linear dynamical systems (LDS)s by gradient descent in imitation learning robotic domains. We introduce a new regularization loss component together with a learning algorithm that improves the stability of the learned autonomous system, by forcing the eigenvalues of the internal state updates of an LDS to be negative reals. We evaluate our approach on a series of real-life and simulated robotic experiments, in comparison to linear and nonlinear Recurrent Neural Network (RNN) architectures. Our results show that our stabilizing method significantly improves test performance of LDS, enabling such linear models to match the performance of contemporary nonlinear RNN architectures. A video of the obstacle avoidance performance of our method on a mobile robot, in unseen environments, compared to other methods can be viewed at https://youtu.be/mhEsCoNao5E.}, author = {Lechner, Mathias and Hasani, Ramin and Rus, Daniela and Grosu, Radu}, booktitle = {Proceedings - IEEE International Conference on Robotics and Automation}, isbn = {9781728173955}, issn = {10504729}, location = {Paris, France}, pages = {5446--5452}, publisher = {IEEE}, title = {{Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme}}, doi = {10.1109/ICRA40945.2020.9196608}, year = {2020}, } @inproceedings{8750, abstract = {Efficiently handling time-triggered and possibly nondeterministic switches for hybrid systems reachability is a challenging task. In this paper we present an approach based on conservative set-based enclosure of the dynamics that can handle systems with uncertain parameters and inputs, where the uncertainties are bound to given intervals. The method is evaluated on the plant model of an experimental electro-mechanical braking system with periodic controller. In this model, the fast-switching controller dynamics requires simulation time scales of the order of nanoseconds. Accurate set-based computations for relatively large time horizons are known to be expensive. However, by appropriately decoupling the time variable with respect to the spatial variables, and enclosing the uncertain parameters using interval matrix maps acting on zonotopes, we show that the computation time can be lowered to 5000 times faster with respect to previous works. This is a step forward in formal verification of hybrid systems because reduced run-times allow engineers to introduce more expressiveness in their models with a relatively inexpensive computational cost.}, author = {Forets, Marcelo and Freire, Daniel and Schilling, Christian}, booktitle = {18th ACM-IEEE International Conference on Formal Methods and Models for System Design}, isbn = {9781728191485}, location = {Virtual Conference}, publisher = {IEEE}, title = {{Efficient reachability analysis of parametric linear hybrid systems with time-triggered transitions}}, doi = {10.1109/MEMOCODE51338.2020.9314994}, year = {2020}, } @inproceedings{8287, abstract = {Reachability analysis aims at identifying states reachable by a system within a given time horizon. This task is known to be computationally expensive for linear hybrid systems. Reachability analysis works by iteratively applying continuous and discrete post operators to compute states reachable according to continuous and discrete dynamics, respectively. In this paper, we enhance both of these operators and make sure that most of the involved computations are performed in low-dimensional state space. In particular, we improve the continuous-post operator by performing computations in high-dimensional state space only for time intervals relevant for the subsequent application of the discrete-post operator. Furthermore, the new discrete-post operator performs low-dimensional computations by leveraging the structure of the guard and assignment of a considered transition. We illustrate the potential of our approach on a number of challenging benchmarks.}, author = {Bogomolov, Sergiy and Forets, Marcelo and Frehse, Goran and Potomkin, Kostiantyn and Schilling, Christian}, booktitle = {Proceedings of the International Conference on Embedded Software}, keywords = {reachability, hybrid systems, decomposition}, location = {Virtual }, title = {{Reachability analysis of linear hybrid systems via block decomposition}}, year = {2020}, } @article{8790, abstract = {Reachability analysis aims at identifying states reachable by a system within a given time horizon. This task is known to be computationally expensive for linear hybrid systems. Reachability analysis works by iteratively applying continuous and discrete post operators to compute states reachable according to continuous and discrete dynamics, respectively. In this article, we enhance both of these operators and make sure that most of the involved computations are performed in low-dimensional state space. In particular, we improve the continuous-post operator by performing computations in high-dimensional state space only for time intervals relevant for the subsequent application of the discrete-post operator. Furthermore, the new discrete-post operator performs low-dimensional computations by leveraging the structure of the guard and assignment of a considered transition. We illustrate the potential of our approach on a number of challenging benchmarks.}, author = {Bogomolov, Sergiy and Forets, Marcelo and Frehse, Goran and Potomkin, Kostiantyn and Schilling, Christian}, issn = {19374151}, journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, number = {11}, pages = {4018--4029}, publisher = {IEEE}, title = {{Reachability analysis of linear hybrid systems via block decomposition}}, doi = {10.1109/TCAD.2020.3012859}, volume = {39}, year = {2020}, } @article{9197, abstract = {In this paper we introduce and study all-pay bidding games, a class of two player, zero-sum games on graphs. The game proceeds as follows. We place a token on some vertex in the graph and assign budgets to the two players. Each turn, each player submits a sealed legal bid (non-negative and below their remaining budget), which is deducted from their budget and the highest bidder moves the token onto an adjacent vertex. The game ends once a sink is reached, and Player 1 pays Player 2 the outcome that is associated with the sink. The players attempt to maximize their expected outcome. Our games model settings where effort (of no inherent value) needs to be invested in an ongoing and stateful manner. On the negative side, we show that even in simple games on DAGs, optimal strategies may require a distribution over bids with infinite support. A central quantity in bidding games is the ratio of the players budgets. On the positive side, we show a simple FPTAS for DAGs, that, for each budget ratio, outputs an approximation for the optimal strategy for that ratio. We also implement it, show that it performs well, and suggests interesting properties of these games. Then, given an outcome c, we show an algorithm for finding the necessary and sufficient initial ratio for guaranteeing outcome c with probability 1 and a strategy ensuring such. Finally, while the general case has not previously been studied, solving the specific game in which Player 1 wins iff he wins the first two auctions, has been long stated as an open question, which we solve.}, author = {Avni, Guy and Ibsen-Jensen, Rasmus and Tkadlec, Josef}, isbn = {9781577358350}, issn = {2374-3468}, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, location = {New York, NY, United States}, number = {02}, pages = {1798--1805}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {{All-pay bidding games on graphs}}, doi = {10.1609/aaai.v34i02.5546}, volume = {34}, year = {2020}, } @inproceedings{8623, abstract = {We introduce the monitoring of trace properties under assumptions. An assumption limits the space of possible traces that the monitor may encounter. An assumption may result from knowledge about the system that is being monitored, about the environment, or about another, connected monitor. We define monitorability under assumptions and study its theoretical properties. In particular, we show that for every assumption A, the boolean combinations of properties that are safe or co-safe relative to A are monitorable under A. We give several examples and constructions on how an assumption can make a non-monitorable property monitorable, and how an assumption can make a monitorable property monitorable with fewer resources, such as integer registers.}, author = {Henzinger, Thomas A and Sarac, Naci E}, booktitle = {Runtime Verification}, isbn = {9783030605070}, issn = {1611-3349}, location = {Los Angeles, CA, United States}, pages = {3--18}, publisher = {Springer Nature}, title = {{Monitorability under assumptions}}, doi = {10.1007/978-3-030-60508-7_1}, volume = {12399}, year = {2020}, } @inproceedings{8195, abstract = {This paper presents a foundation for refining concurrent programs with structured control flow. The verification problem is decomposed into subproblems that aid interactive program development, proof reuse, and automation. The formalization in this paper is the basis of a new design and implementation of the Civl verifier.}, author = {Kragl, Bernhard and Qadeer, Shaz and Henzinger, Thomas A}, booktitle = {Computer Aided Verification}, isbn = {9783030532871}, issn = {1611-3349}, pages = {275--298}, publisher = {Springer Nature}, title = {{Refinement for structured concurrent programs}}, doi = {10.1007/978-3-030-53288-8_14}, volume = {12224}, year = {2020}, } @inproceedings{8012, abstract = {Asynchronous programs are notoriously difficult to reason about because they spawn computation tasks which take effect asynchronously in a nondeterministic way. Devising inductive invariants for such programs requires understanding and stating complex relationships between an unbounded number of computation tasks in arbitrarily long executions. In this paper, we introduce inductive sequentialization, a new proof rule that sidesteps this complexity via a sequential reduction, a sequential program that captures every behavior of the original program up to reordering of coarse-grained commutative actions. A sequential reduction of a concurrent program is easy to reason about since it corresponds to a simple execution of the program in an idealized synchronous environment, where processes act in a fixed order and at the same speed. We have implemented and integrated our proof rule in the CIVL verifier, allowing us to provably derive fine-grained implementations of asynchronous programs. We have successfully applied our proof rule to a diverse set of message-passing protocols, including leader election protocols, two-phase commit, and Paxos.}, author = {Kragl, Bernhard and Enea, Constantin and Henzinger, Thomas A and Mutluergil, Suha Orhun and Qadeer, Shaz}, booktitle = {Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation}, isbn = {9781450376136}, location = {London, United Kingdom}, pages = {227--242}, publisher = {Association for Computing Machinery}, title = {{Inductive sequentialization of asynchronous programs}}, doi = {10.1145/3385412.3385980}, year = {2020}, }