Rajeev Alur's research while affiliated with University of Pennsylvania and other places
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Publications (381)
Many future technologies rely on neural networks, but verifying the correctness of their behavior remains a major challenge. It is known that neural networks can be fragile in the presence of even small input perturbations, yielding unpredictable outputs. The verification of neural networks is therefore vital to their adoption, and a number of appr...
Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training joint policies that form a Nash equilibrium. In our approach, rather than providing low-level reward...
Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data. As calculating Lipschitz constants is NP-hard, techniques for estimating Lipschitz constants must navigate the trade-off between scalability and accuracy. In this work, we sig...
Debugging imperative network programs is a difficult task for operators as it requires understanding various network modules and complicated data structures. For this purpose, this paper presents an automated technique for repairing network programs with respect to unit tests. Given as input a faulty network program and a set of unit tests, our app...
Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training joint policies that form a Nash equilibrium. In our approach, rather than providing low-level reward...
Nested words allow modeling of linear and hierarchical structure in data, and nested word automata are special kinds of pushdown automata whose push/pop actions are directed by the hierarchical structure in the input nested word. The resulting class of regular languages of nested words has many appealing theoretical properties, and has found many a...
Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of...
Reactive synthesis algorithms allow automatic construction of policies to control an environment modeled as a Markov Decision Process (MDP) that are optimal with respect to high-level temporal logic specifications assuming the MDP model is known a priori. Reinforcement learning algorithms, in contrast, are designed to learn an optimal policy when t...
Debugging imperative network programs is a challenging task for developers because understanding various network modules and complicated data structures is typically time-consuming. To address the challenge, this paper presents an automated technique for repairing network programs from unit tests. Specifically, given as input a faulty network progr...
This paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2...
This paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2...
We study the problem of learning control policies for complex tasks given by logical specifications Typically, these approaches automatically generate a reward function from a given specification and use a suitable reinforcement learning algorithm to learn a policy that maximizes the expected reward. These approaches, however, scale poorly to compl...
We study the problem of learning control policies for complex tasks given by logical specifications. Recent approaches automatically generate a reward function from a given specification and use a suitable reinforcement learning algorithm to learn a policy that maximizes the expected reward. These approaches, however, scale poorly to complex tasks...
Real-time data processing applications with low latency requirements have led to the increasing popularity of stream processing systems. While such systems offer convenient APIs that can be used to achieve data parallelism automatically, they offer limited support for computations which require synchronization between parallel nodes. In this paper...
Writing classification rules to identify interesting network traffic is a time-consuming and error-prone task. Learning-based classification systems automatically extract such rules from positive and negative traffic examples. However, due to limitations in the representation of network traffic and the learning strategy, these systems lack both exp...
GPU programming has become popular due to the high computational capabilities of GPUs. Obtaining significant performance gains with GPU is however challenging and the programmer needs to be aware of various subtleties of the GPU architecture. One such subtlety lies in accessing GPU memory, where certain access patterns can lead to poor performance....
This paper addresses the problem of verifying the safety of autonomous systems with neural network (NN) controllers. We focus on NNs with sigmoid/tanh activations and use the fact that the sigmoid/tanh is the solution to a quadratic differential equation. This allows us to convert the NN into an equivalent hybrid system and cast the problem as a hy...
We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces. In our framework, the user specifies subgoal regions which are subsets of states; then, we (i) learn options that serve as transitions between these subgoal regions, and (ii) construct a high-level plan in the resulting abstract dec...
Writing classification rules to identify malicious network traffic is a time-consuming and error-prone task. Learning-based classification systems automatically extract such rules from positive and negative traffic examples. However, due to limitations in the representation of network traffic and the learning strategy, these systems lack both expre...
Reinforcement learning is a promising approach for learning control policies for robot tasks. However, specifying complex tasks (e.g., with multiple objectives and safety constraints) can be challenging, since the user must design a reward function that encodes the entire task. Furthermore, the user often needs to manually shape the reward to ensur...
The shortage of people trained in STEM fields is becoming acute, and universities and colleges are straining to satisfy this demand. In the case of computer science, for instance, the number of US students taking introductory courses has grown three-fold in the past decade. Recently, massive open online courses (MOOCs) have been promoted as a way t...
This paper describes a verification case study on an autonomous racing car with a neural network (NN) controller. Although several verification approaches have been recently proposed, they have only been evaluated on low-dimensional systems or systems with constrained environments. To explore the limits of existing approaches, we present a challeng...
Motivated by real-time monitoring and data processing applications, we develop a formal theory of quantitative queries for streaming data that can be evaluated efficiently. We consider the model of unambiguous Cost Register Automata (CRAs), which are machines that combine finite-state control (for identifying regular patterns) with a finite set of...
This paper describes a verification case study on an autonomous racing car with a neural network (NN) controller. Although several verification approaches have been proposed over the last year, they have only been evaluated on low-dimensional systems or systems with constrained environments. To explore the limits of existing approaches, we present...
Information flow analysis is an effective way to check useful security properties, such as whether secret information can leak to adversaries. Despite being widely investigated in the realm of programming languages, information-flow-based security analysis has not been widely studied in the domain of cyber-physical systems (CPS). CPS provide intere...
Information flow analysis is an effective way to check useful security properties, such as whether secret information can leak to adversaries. Despite being widely investigated in the realm of programming languages, information-flow- based security analysis has not been widely studied in the domain of cyber-physical systems (CPS). CPS provide inter...
Distributed architectures for efficient processing of streaming data are increasingly critical to modern information processing systems. The goal of this paper is to develop type-based programming abstractions that facilitate correct and efficient deployment of a logical specification of the desired computation on such architectures. In the propose...
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. We focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid sys...
Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation $f$ that meets both a semantic constraint given by a logical formula $\phi$ in a background theory $\mathbb{T}$, and a syntactic constraint given by a grammar $G$, which specifies the allowed set of candidate implementations. Such a synthesis problem can be fo...
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. We focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid sys...
Model-based design offers a promising approach for assisting developers to build reliable and secure cyber-physical systems (CPSs) in a systematic manner. In this methodology, a designer first constructs a model, with mathematically precise semantics, of the system under design, and performs extensive analysis with respect to correctness requiremen...
In real-time decision making and runtime monitoring applications, declarative languages are commonly used as they facilitate modular high-level specifications with the compiler guaranteeing evaluation over data streams in an efficient and incremental manner. We introduce the model of Data Transducers to allow modular compilation of queries over str...
We illustrate the ingredients of the state-of-the-art of model-based approach for the formal design and verification of cyber-physical systems. To capture the interaction between a discrete controller and its continuously evolving environment, we use the formal models of timed and hybrid automata. We explain the steps of modeling and verification i...
Writing programs that are both correct and efficient is challenging. A potential solution lies in program synthesis aimed at automatic derivation of an executable implementation (the “how”) from a high-level logical specification of the desired input-to-output behavior (the “what”). A mature synthesis technology can have a transformative impact on...
![Figure 3: Mountain Car problem [23]. The car needs to drive up the left...](profile/Radoslav-Ivanov/publication/328758266/figure/fig2/AS:689813292326912@1541475713097/Mountain-Car-problem-23-The-car-needs-to-drive-up-the-left-hill-first-in-order-to_Q320.jpg)
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. Although techniques exist for verifying input/output properties of the neural network itself, these methods cannot be used to verify properties of the closed-loop system (since they work with piecewise-li...
![Figure 3: Mountain Car problem [23]. The car needs to drive up the left...](profile/George-Pappas-2/publication/328772322/figure/fig2/AS:690049532321796@1541532037456/Mountain-Car-problem-23-The-car-needs-to-drive-up-the-left-hill-first-in-order-to_Q320.jpg)
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. Although techniques exist for verifying input/output properties of the neural network itself, these methods cannot be used to verify properties of the closed-loop system (since they work with piecewise-li...
Synthesis of finite-state controllers from high-level specifications in multi-agent systems can be reduced to solving multi-player concurrent games over finite graphs. The complexity of solving such games with qualitative objectives for agents, such as reaching a target set, is well understood resulting in tools with applications in robotics. In th...
Optimizing GPU programs by tuning execution parameters is essential to realizing the full performance potential of GPU hardware. However, many of these optimizations do not ensure correctness and subtle errors can enter while optimizing a GPU program. Further, lack of formal models and the presence of non-trivial transformations prevent verificatio...
Recent emergence of software-defined networks offers an opportunity to design domain-specific programming abstractions aimed at network operators. In this paper, we propose scenario-based programming, a framework that allows network operators to program network policies by describing example behaviors in representative scenarios. Given these scenar...
Efficient processing of input data streams is central to IoT systems, and the goal of this paper is to develop a logical foundation for specifying the computation of such stream processing. In the proposed model, both the input and output of a stream processing system consists of tagged data items with a dependency relation over tags that captures...
Motivated by real-time monitoring and data processing applications, we develop a formal theory of quantitative queries for streaming data that can be evaluated efficiently. We consider the model of unambiguous Cost Register Automata (CRAs), which are machines that combine finite-state control (for identifying regular patterns) with a finite set of...
A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the gr...
A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the gr...
We consider the model-checking problem for sequential programs with procedure calls. We first present basic algorithms for solving the reachability problem and the fair computation problem. The algorithms are based on two techniques: summarization, which computes reachability information by solving a set of fixpoint equations, and saturation, which...
We consider the controller synthesis problem for multi-agent systems that consist of a set of controlled and uncontrolled agents. Controlled agents may need to cooperate with each other and react to actions of uncontrolled agents in order to fulfill their objectives. Moreover, agents may be imperfect, i.e., only partially observe their environment....
As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languag...
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula phi in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in...
In network management today, dynamic updates are required for traffic engineering and for timely response to security threats. Decisions for such updates are based on monitoring network traffic to compute numerical quantities based on a variety of network and application-level performance metrics. Today's state-of-the-art tools lack programming abs...
Quantitative regular expressions (QREs) have been recently proposed as a high-level declarative language for specifying complex numerical queries over data streams in a modular way. QREs have appealing theoretical properties, and each QRE can be compiled into an efficient streaming algorithm for its evaluation. In this paper, we generalize the noti...
Graphics Processing Units (GPUs) have become widespread and popular over the past decade. Fully utilizing the parallel compute and memory resources that GPUs present remains a significant challenge, however. In this paper, we describe GPUDrano: a scalable static analysis that detects uncoalesced global memory accesses in CUDA programs. Uncoalesced...
Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a nove...
Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a nove...
Bounded-rate multimode systems are hybrid systems that switch freely among a finite set of modes, and whose dynamics are specified by a finite number of real-valued variables with mode-dependent rates that vary within given bounded sets. The scheduler repeatedly proposes a time and a mode, while the environment chooses an allowable rate for that mo...
Given a semantic constraint specified by a logical formula, and a syntactic constraint specified by a context-free grammar, the Syntax-Guided Synthesis (SyGuS) problem is to find an expression that satisfies both the syntactic and semantic constraints. An enumerative approach to solve this problem is to systematically generate all expressions from...
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula φ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in Sy...
In this paper we consider the controller synthesis problem for multi-agent systems that consist of a set of controlled and uncontrolled agents. Controlled agents may need to cooperate with each other and react to the actions of uncontrolled agents in order to fulfill their objectives. Besides, the controlled agents may be imperfect, i.e., only part...
In this work we build on these models to look at social influence from a strategic perspective. We do so by introducing a new class of games, called games of influence. Specifically, a game of influence is an infinite repeated game with incomplete information in which, at each stage of interaction, an agent can make her opinions visible (public) or...
Reactive synthesis with the ambitious goal of automatically synthesizing correct-by-construction controllers from high-level specifications, has recently attracted significant attention in system design and control. In practice, complex systems are often not constructed from scratch but from a set of existing building blocks. For example in robot m...
We propose quantitative regular expressions (QREs) as a high-level programming abstraction for specifying complex numerical queries over data streams in a modular way. Our language allows the arbitrary nesting of orthogonal sets of combinators: (a) generalized versions of choice, concatenation, and Kleene-iteration from regular expressions, (b) str...
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula Phi in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in...
Recent emergence of software-defined networks offers an opportunity to design domain-specific programming abstractions aimed at network operators. In this paper, we propose scenario-based programming, a framework that allows network operators to program network policies by describing representative example behaviors. Given these scenarios, our synt...
Nested words allow modeling of linear and hierarchical structure
in data, and nested word automata are special kinds of pushdown
automata whose push/pop actions are directed by the hierarchical structure
in the input nested word. The resulting class of regular languages of
nested words has many appealing theoretical properties, and has found
many a...
Given a specification and a set of candidate programs (program space), the program synthesis problem is to find a candidate program that satisfies the specification. We present the synthesis through unification (STUN) approach, which is an extension of the counter-example guided inductive synthesis (CEGIS) approach. In CEGIS, the synthesizer mainta...
A distributed protocol is typically modeled as a set of communicating
processes, where each process is described as an extended state machine along
with fairness assumptions, and its correctness is specified using safety and
liveness requirements. Designing correct distributed protocols is a challenging
task. Aimed at simplifying this task, we allo...
We consider the problem of compositional refinement of components’ specifications in the context of compositional reactive synthesis. Our solution is based on automatic refinement of assumptions and guarantees expressed in linear temporal logic (LTL). We show how behaviors of the environment and the system can be inferred from counter-strategies an...
In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of stu-dents' mistakes. We focus on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a st...