arXiv subjects

Ernesto Casablanca

Publications and source records attributed to Ernesto Casablanca.

4 recordsLinked to original sources

arXiv2026

Verified Linear Programming through Tolerance-Aware Precision Boosting

Linear programming plays a fundamental role in computer science, with applications in optimization, formal verification, SMT solving, and numerous other domains. When exactness guarantees are required, numerical inaccuracies arising from floating-point arithmetic can compromise soundness, whereas exact rational arithmetic often incurs significant computational overhead. In this paper, we investigate the numerical stability of the simplex algorithm and establish conditions under which a precision-boosting floating-point implementation provably produces the same pivot decisions and final basis as an exact rational implementation, from which the final result is reconstructed and certified exactly. Our analysis shows that correctness depends on both arithmetic precision and a careful handling of numerical tolerances. Based on these results, we develop a tolerance-aware, precision-boosting simplex algorithm with formal correctness guarantees. Finally, we introduce a delta-complete termination criterion that allows the algorithm to terminate once certified upper and lower bounds on the optimal objective differ by at most a user-specified threshold delta, providing a certified, user-controlled optimality gap.

math.OC↗

arXiv2025

LUCID: Learning-Enabled Uncertainty-Aware Certification of Stochastic Dynamical Systems

Ensuring the safety of AI-enabled systems, particularly in high-stakes domains such as autonomous driving and healthcare, has become increasingly critical. Traditional formal verification tools fall short when faced with systems that embed both opaque, black-box AI components and complex stochastic dynamics. To address these challenges, we introduce LUCID (Learning-enabled Uncertainty-aware Certification of stochastIc Dynamical systems), a verification engine for certifying safety of black-box stochastic dynamical systems from a finite dataset of random state transitions. As such, LUCID is the first known tool capable of establishing quantified safety guarantees for such systems. Thanks to its modular architecture and extensive documentation, LUCID is designed for easy extensibility. LUCID employs a data-driven methodology rooted in control barrier certificates, which are learned directly from system transition data, to ensure formal safety guarantees. We use conditional mean embeddings to embed data into a reproducing kernel Hilbert space (RKHS), where an RKHS ambiguity set is constructed that can be inflated to robustify the result to out-of-distribution behavior. A key innovation within LUCID is its use of a finite Fourier kernel expansion to reformulate a semi-infinite non-convex optimization problem into a tractable linear program. The resulting spectral barrier allows us to leverage the fast Fourier transform to generate the relaxed problem efficiently, offering a scalable yet distributionally robust framework for verifying safety. LUCID thus offers a robust and efficient verification framework, able to handle the complexities of modern black-box systems while providing formal guarantees of safety. These unique capabilities are demonstrated on challenging benchmarks.

eess.SY↗

arXiv2025

Blending Participatory Design and Artificial Awareness for Trustworthy Autonomous Vehicles

Current robotic agents, such as autonomous vehicles (AVs) and drones, need to deal with uncertain real-world environments with appropriate situational awareness (SA), risk awareness, coordination, and decision-making. The SymAware project strives to address this issue by designing an architecture for artificial awareness in multi-agent systems, enabling safe collaboration of autonomous vehicles and drones. However, these agents will also need to interact with human users (drivers, pedestrians, drone operators), which in turn requires an understanding of how to model the human in the interaction scenario, and how to foster trust and transparency between the agent and the human. In this work, we aim to create a data-driven model of a human driver to be integrated into our SA architecture, grounding our research in the principles of trustworthy human-agent interaction. To collect the data necessary for creating the model, we conducted a large-scale user-centered study on human-AV interaction, in which we investigate the interaction between the AV's transparency and the users' behavior. The contributions of this paper are twofold: First, we illustrate in detail our human-AV study and its findings, and second we present the resulting Markov chain models of the human driver computed from the study's data. Our results show that depending on the AV's transparency, the scenario's environment, and the users' demographics, we can obtain significant differences in the model's transitions.

cs.RO↗

arXiv2024

SymAware: A Software Development Framework for Trustworthy Multi-Agent Systems with Situational Awareness

Developing trustworthy multi-agent systems for practical applications is challenging due to the complicated communication of situational awareness (SA) among agents. This paper showcases a novel efficient and easy-to-use software framework for multi-agent simulation, named SymAware which provides a rich set of predefined data structures to compute, store, and communicate SA for agents. It also provides an abstract interface for the agents to compute their control inputs taking into account the awareness of the situation, knowledge, and risk of surrounding agents. Besides, utilizing a cluster of specialized components, SymAware hides the heavy computation of physical rendering and communication interfacing of simulation engines behind the control threads, resulting in high implementation efficiency in bridging the gap between conceptual prototyping and practical applications. Three multi-agent case studies are used to validate the efficacy and efficiency of this software framework.

cs.RO↗