Formal Verification of Neural Network Controlled Autonomous Systems

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial conditions such that a robot trajectory starting from these initial conditions is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of the safe initial states. The first technical problem in computing the finite state abstraction is to mathematically model the imaging function that maps the robot position to the LiDAR image. To that end, we introduce the notion of imaging-adapted sets as partitions of the workspace in which the imaging function is guaranteed to be affine. We develop a polynomial-time algorithm to partition the workspace into imaging-adapted sets along with computing the corresponding affine imaging functions. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) nonlinearity, the second technical challenge is to analyze the behavior of the neural network. To that end, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible segments of different ReLUs. SMC solvers then use a Boolean satisfiability solver and a convex programming solver and decompose the problem into smaller subproblems. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space feasible ReLU segments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with increasing complexity of the neural network controller

A Review of Formal Methods applied to Machine Learning

We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems

An Algebra of Hierarchical Graphs

We define an algebraic theory of hierarchical graphs, whose axioms characterise graph isomorphism: two terms are equated exactly when they represent the same graph. Our algebra can be understood as a high-level language for describing graphs with a node-sharing, embedding structure, and it is then well suited for defining graphical representations of software models where nesting and linking are key aspects

Scalable Verification of GNN-based Job Schedulers

Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive performance, concerns remain over whether these GNN-based job schedulers meet users' expectations about other important properties, such as strategy-proofness, sharing incentive, and stability. In this work, we consider formal verification of GNN-based job schedulers. We address several domain-specific challenges such as networks that are deeper and specifications that are richer than those encountered when verifying image and NLP classifiers. We develop vegas, the first general framework for verifying both single-step and multi-step properties of these schedulers based on carefully designed algorithms that combine abstractions, refinements, solvers, and proof transfer. Our experimental results show that vegas achieves significant speed-up when verifying important properties of a state-of-the-art GNN-based scheduler compared to previous methods.Comment: Condensed version published at OOPSLA'2

From Network Interface to Multithreaded Web Applications: A Case Study in Modular Program Verification

Many verifications of realistic software systems are monolithic, in the sense that they define single global invariants over complete system state. More modular proof techniques promise to support reuse of component proofs and even reduce the effort required to verify one concrete system, just as modularity simplifies standard software development. This paper reports on one case study applying modular proof techniques in the Coq proof assistant. To our knowledge, it is the first modular verification certifying a system that combines infrastructure with an application of interest to end users. We assume a nonblocking API for managing TCP networking streams, and on top of that we work our way up to certifying multithreaded, database-backed Web applications. Key verified components include a cooperative threading library and an implementation of a domain-specific language for XML processing. We have deployed our case-study system on mobile robots, where it interfaces with off-the-shelf components for sensing, actuation, and control.National Science Foundation (U.S.) (Grant CCF-1253229)United States. Defense Advanced Research Projects Agency (Agreement FA8750-12-2-0293

From Network Interface to Multithreaded Web Applications: A Case Study in Modular Program Verification

Many verifications of realistic software systems are monolithic, in the sense that they define single global invariants over complete system state. More modular proof techniques promise to support reuse of component proofs and even reduce the effort required to verify one concrete system, just as modularity simplifies standard software development. This paper reports on one case study applying modular proof techniques in the Coq proof assistant. To our knowledge, it is the first modular verification certifying a system that combines infrastructure with an application of interest to end users. We assume a nonblocking API for managing TCP networking streams, and on top of that we work our way up to certifying multithreaded, database-backed Web applications. Key verified components include a cooperative threading library and an implementation of a domain-specific language for XML processing. We have deployed our case-study system on mobile robots, where it interfaces with off-the-shelf components for sensing, actuation, and control.National Science Foundation (U.S.) (NSF grant CCF-1253229)United States. Defense Advanced Research Projects Agency (DARPA, agreement number FA8750-12-2-0293