arXiv subjects

Houssam Abbas

Publications and source records attributed to Houssam Abbas.

At least 19 recordsLinked to original sources

arXiv2026

Runtime Monitoring of Distributed Cyber-Physical Systems Without a Global Clock

We give the first theoretical characterization, and the first algorithm, for continuous monitoring of a distributed Cyber-Physical System (CPS) against a dense-time temporal logic specification. A distributed CPS is composed of multiple agents, each with a local clock; these clocks drift from each other, so there is no well-defined global time. When monitoring such a system's output signal against a temporal logic specification, it is not evident how to interpret the temporal constraints of the formula, and what satisfaction means. Yet CPS designers, like control engineers, typically think of their system's operation in terms of global time. Most existing techniques for monitoring distributed systems work with discrete-time specifications not suitable for CPS, and/or require an explicit mapping of temporal constraints to local clocks. We introduce an algorithm that addresses the above challenges for a fragment of Signal Temporal Logic (STL) that still includes all temporal operators. It relies on a novel extension of satisfaction signals to this partially synchronous setting (where clocks drift), and an analysis of the geometry of multi-dimensional partially synchronous time. The algorithm returns the set of all possible global moments that can satisfy the specification. Knowledge of these possible global moments is important for debugging distributed hybrid control systems such as fleets of drones and electrical grids. We derive the worst-case complexity of the algorithm, and implement a sound approximation of it that experimentally illustrates effective monitoring, even in scenarios of up to 50 agents.

cs.LO↗

arXiv2025

Fair-CoPlan: Negotiated Flight Planning with Fair Deconfliction for Urban Air Mobility

Urban Air Mobility (UAM) is an emerging transportation paradigm in which Uncrewed Aerial Systems (UAS) autonomously transport passengers and goods in cities. The UAS have different operators with different, sometimes competing goals, yet must share the airspace. We propose a negotiated, semi-distributed flight planner that optimizes UAS' flight lengths {\em in a fair manner}. Current flight planners might result in some UAS being given disproportionately shorter flight paths at the expense of others. We introduce Fair-CoPlan, a planner in which operators and a Provider of Service to the UAM (PSU) together compute \emph{fair} flight paths. Fair-CoPlan has three steps: First, the PSU constrains take-off and landing choices for flights based on capacity at and around vertiports. Then, operators plan independently under these constraints. Finally, the PSU resolves any conflicting paths, optimizing for path length fairness. By fairly spreading the cost of deconfliction Fair-CoPlan encourages wider participation in UAM, ensures safety of the airspace and the areas below it, and promotes greater operator flexibility. We demonstrate Fair-CoPlan through simulation experiments and find fairer outcomes than a non-fair planner with minor delays as a trade-off.

cs.RO↗

arXiv2025

FiReFly: Fair Distributed Receding Horizon Planning for Multiple UAVs

We propose injecting notions of fairness into multi-robot motion planning. When robots have competing interests, it is important to optimize for some kind of fairness in their usage of resources. In this work, we explore how the robots' energy expenditures might be fairly distributed among them, while maintaining mission success. We formulate a distributed fair motion planner and integrate it with safe controllers in a algorithm called FiReFly. For simulated reach-avoid missions, FiReFly produces fairer trajectories and improves mission success rates over a non-fair planner. We find that real-time performance is achievable up to 15 UAVs, and that scaling up to 50 UAVs is possible with trade-offs between runtime and fairness improvements.

cs.RO↗

arXiv2025

PRREACH: Probabilistic Risk Assessment Using Reachability for UAV Control

We present a new approach for designing risk-bounded controllers for Uncrewed Aerial Vehicles (UAVs). Existing frameworks for assessing risk of UAV operations rely on knowing the conditional probability of an incident occurring given different causes. Limited data for computing these probabilities makes real-world implementation of these frameworks difficult. Furthermore, existing frameworks do not include control methods for risk mitigation. Our approach relies on UAV dynamics, and employs reachability analysis for a probabilistic risk assessment over all feasible UAV trajectories. We use this holistic risk assessment to formulate a control optimization problem that minimally changes a UAV's existing control law to be bounded by an accepted risk threshold. We call our approach PRReach. Public and readily available UAV dynamics models and open source spatial data for mapping hazard outcomes enables practical implementation of PRReach for both offline pre-flight and online in-flight risk assessment and mitigation. We evaluate PRReach through simulation experiments on real-world data. Results show that PRReach controllers reduce risk by up to 24% offline, and up to 53% online from classical controllers.

eess.SY↗

arXiv2025

Deontically Constrained Policy Improvement in Reinforcement Learning Agents

Markov Decision Processes (MDPs) are the most common model for decision making under uncertainty in the Machine Learning community. An MDP captures non-determinism, probabilistic uncertainty, and an explicit model of action. A Reinforcement Learning (RL) agent learns to act in an MDP by maximizing a utility function. This paper considers the problem of learning a decision policy that maximizes utility subject to satisfying a constraint expressed in deontic logic. In this setup, the utility captures the agent's mission - such as going quickly from A to B. The deontic formula represents (ethical, social, situational) constraints on how the agent might achieve its mission by prohibiting classes of behaviors. We use the logic of Expected Act Utilitarianism, a probabilistic stit logic that can be interpreted over controlled MDPs. We develop a variation on policy improvement, and show that it reaches a constrained local maximum of the mission utility. Given that in stit logic, an agent's duty is derived from value maximization, this can be seen as a way of acting to simultaneously maximize two value functions, one of which is implicit, in a bi-level structure. We illustrate these results with experiments on sample MDPs.

cs.AI↗

arXiv2025

Monitoring in the Dark: Privacy-Preserving Runtime Verification of Cyber-Physical Systems

In distributed Cyber-Physical Systems and Internet-of-Things applications, the nodes of the system send measurements to a monitor that checks whether these measurements satisfy given formal specifications. For instance in Urban Air Mobility, a local traffic authority will be monitoring drone traffic to evaluate its flow and detect emerging problematic patterns. Certain applications require both the specification and the measurements to be private -- i.e. known only to their owners. Examples include traffic monitoring, testing of integrated circuit designs, and medical monitoring by wearable or implanted devices. In this paper we propose a protocol that enables privacy-preserving robustness monitoring. By following our protocol, both system (e.g. drone) and monitor (e.g. traffic authority) only learn the robustness of the measured trace w.r.t. the specification. But the system learns nothing about the formula, and the monitor learns nothing about the signal monitored. We do this using garbled circuits, for specifications in Signal Temporal Logic interpreted over timed state sequences. We analyze the runtime and memory overhead of privacy preservation, the size of the circuits, and their practicality for three different usage scenarios: design testing, offline monitoring, and online monitoring of Cyber-Physical Systems.

cs.LO↗

arXiv2024

ROS2-Based Simulation Framework for Cyberphysical Security Analysis of UAVs

We present a new simulator of Uncrewed Aerial Vehicles (UAVs) that is tailored to the needs of testing cyber-physical security attacks and defenses. Recent investigations into UAV safety have unveiled various attack surfaces and some defense mechanisms. However, due to escalating regulations imposed by aviation authorities on security research on real UAVs, and the substantial costs associated with hardware test-bed configurations, there arises a necessity for a simulator capable of substituting for hardware experiments, and/or narrowing down their scope to the strictly necessary. The study of different attack mechanisms requires specific features in a simulator. We propose a simulation framework based on ROS2, leveraging some of its key advantages, including modularity, replicability, customization, and the utilization of open-source tools such as Gazebo. Our framework has a built-in motion planner, controller, communication models and attack models. We share examples of research use cases that our framework can enable, demonstrating its utility.

cs.RO↗

arXiv2024

Formal Ethical Obligations in Reinforcement Learning Agents: Verification and Policy Updates

When designing agents for operation in uncertain environments, designers need tools to automatically reason about what agents ought to do, how that conflicts with what is actually happening, and how a policy might be modified to remove the conflict. These obligations include ethical and social obligations, permissions and prohibitions, which constrain how the agent achieves its mission and executes its policy. We propose a new deontic logic, Expected Act Utilitarian deontic logic, for enabling this reasoning at design time: for specifying and verifying the agent's strategic obligations, then modifying its policy from a reference policy to meet those obligations. Unlike approaches that work at the reward level, working at the logical level increases the transparency of the trade-offs. We introduce two algorithms: one for model-checking whether an RL agent has the right strategic obligations, and one for modifying a reference decision policy to make it meet obligations expressed in our logic. We illustrate our algorithms on DAC-MDPs which accurately abstract neural decision policies, and on toy gridworld environments.

cs.AI↗

arXiv2024

Defending Large Language Models Against Attacks With Residual Stream Activation Analysis

The widespread adoption of Large Language Models (LLMs), exemplified by OpenAI's ChatGPT, brings to the forefront the imperative to defend against adversarial threats on these models. These attacks, which manipulate an LLM's output by introducing malicious inputs, undermine the model's integrity and the trust users place in its outputs. In response to this challenge, our paper presents an innovative defensive strategy, given white box access to an LLM, that harnesses residual activation analysis between transformer layers of the LLM. We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification. We curate multiple datasets to demonstrate how this method of classification has high accuracy across multiple types of attack scenarios, including our newly-created attack dataset. Furthermore, we enhance the model's resilience by integrating safety fine-tuning techniques for LLMs in order to measure its effect on our capability to detect attacks. The results underscore the effectiveness of our approach in enhancing the detection and mitigation of adversarial inputs, advancing the security framework within which LLMs operate.

cs.CR↗

arXiv2023

Decentralized Predicate Detection over Partially Synchronous Continuous-Time Signals

We present the first decentralized algorithm for detecting predicates over continuous-time signals under partial synchrony. A distributed cyber-physical system (CPS) consists of a network of agents, each of which measures (or computes) a continuous-time signal. Examples include distributed industrial controllers connected over wireless networks and connected vehicles in traffic. The safety requirements of such CPS, expressed as logical predicates, must be monitored at runtime. This monitoring faces three challenges: first, every agent only knows its own signal, whereas the safety requirement is global and carries over multiple signals. Second, the agents' local clocks drift from each other, so they do not even agree on the time. Thus, it is not clear which signal values are actually synchronous to evaluate the safety predicate. Third, CPS signals are continuous-time so there are potentially uncountably many safety violations to be reported. In this paper, we present the first decentralized algorithm for detecting conjunctive predicates in this setup. Our algorithm returns all possible violations of the predicate, which is important for eliminating bugs from distributed systems regardless of actual clock drift. We prove that this detection algorithm is in the same complexity class as the detector for discrete systems. We implement our detector and validate it experimentally.

cs.LO↗

arXiv2022

Differentiable Inference of Temporal Logic Formulas

We demonstrate the first Recurrent Neural Network architecture for learning Signal Temporal Logic formulas, and present the first systematic comparison of formula inference methods. Legacy systems embed much expert knowledge which is not explicitly formalized. There is great interest in learning formal specifications that characterize the ideal behavior of such systems -- that is, formulas in temporal logic that are satisfied by the system's output signals. Such specifications can be used to better understand the system's behavior and improve design of its next iteration. Previous inference methods either assumed certain formula templates, or did a heuristic enumeration of all possible templates. This work proposes a neural network architecture that infers the formula structure via gradient descent, eliminating the need for imposing any specific templates. It combines learning of formula structure and parameters in one optimization. Through systematic comparison, we demonstrate that this method achieves similar or better mis-classification rates (MCR) than enumerative and lattice methods. We also observe that different formulas can achieve similar MCR, empirically demonstrating the under-determinism of the problem of temporal logic inference.

cs.LG↗

arXiv2021

Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations

We develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permissions and prohibitions are distinct from a system's mission, and are a necessary part of specifying advanced, adaptive AI-equipped systems. They need a dedicated deontic logic of obligations to formalize them. Most existing deontic logics lack corresponding algorithms and system models that permit automatic verification. We demonstrate how a particular deontic logic, Dominance Act Utilitarianism (DAU), is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We demonstrate its usefulness by formalizing a subset of Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for how self-driving cars should and should not behave in traffic. We show that certain logical consequences of RSS are undesirable, indicating a need to further refine the proposal. We also demonstrate how obligations can change over time, which is necessary for long-term autonomy. We then demonstrate a model-checking algorithm for DAU formulas on weighted transition systems, and illustrate it by model-checking obligations of a self-driving car controller from the literature.

cs.AI↗

arXiv2021

Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme

Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e.g. moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-'N-Flying (LNF) a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on-the-fly and allows autonomous UAS managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UAS as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining: a) learning-based decision-making, and b) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UAS on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds), and under certain assumptions has failure rates of less than 1% in the worst-case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.

eess.SY↗

arXiv2020

Logical Signal Processing: a Fourier Analysis of Temporal Logic

What is the frequency content of temporal logic formulas? That is, when we monitor a signal against a formula, which frequency bands of the signal are relevant to the logic and should be preserved, and which can be safely discarded? This question is relevant whenever signals are filtered or compressed before being monitored, which is almost always the case for analog signals. To answer this question, we focus on monitors that measure the robustness of a signal relative to a specification in Signal Temporal Logic. We prove that robustness monitors can be modeled using Volterra series. We then study the Fourier transforms of these Volterra representations, and provide a method to derive the Fourier transforms of entire formulas. We also make explicit the measurement process in temporal logic and re-define it on the basis of distributions to make it compatible with measurements in signal processing. Experiments illustrate these results. Beyond compression, this work enables the integration of temporal logic monitoring into common signal processing toolchains as just another signal processing operation, and enables a common formalism to study both logical and non-logical operations in the frequency domain, which we refer to as Logical Signal Processing.

eess.SY↗

arXiv2020

A Deontic Logic Analysis of Autonomous Systems' Safety

We consider the pressing question of how to model, verify, and ensure that autonomous systems meet certain \textit{obligations} (like the obligation to respect traffic laws), and refrain from impermissible behavior (like recklessly changing lanes). Temporal logics are heavily used in autonomous system design; however, as we illustrate here, temporal (alethic) logics alone are inappropriate for reasoning about obligations of autonomous systems. This paper proposes the use of Dominance Act Utilitarianism (DAU), a deontic logic of agency, to encode and reason about obligations of autonomous systems. We use DAU to analyze Intel's Responsibility-Sensitive Safety (RSS) proposal as a real-world case study. We demonstrate that DAU can express well-posed RSS rules, formally derive undesirable consequences of these rules, illustrate how DAU could help design systems that have specific obligations, and how to model-check DAU obligations.

cs.LO↗

arXiv2020

FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace

We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others - unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses the fair planning question for general autonomous systems, including UAV fleets, subject to complex missions typical of urban applications. FairFly formalizes each mission in temporal logic. An offline search finds the fairest paths that satisfy the missions and can be flown by the UAVs, leading to lighter online control load. It allows explicit negotiation between UAVs to enable imbalanced path durations if desired. We present three fairness notions, including one that reduces energy consumption. We validate our results in simulation, and demonstrate a lighter computational load and less UAV energy consumption as a result of flying fair trajectories.

eess.SY↗

arXiv2020

Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility

With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities. Unlike traditional human-in-the-loop air traffic management, UAM requires decentralized autonomous approaches that scale for an order of magnitude higher aircraft densities and are applicable to urban settings. We present Learning-to-Fly (L2F), a decentralized on-demand airborne collision avoidance framework for multiple UAS that allows them to independently plan and safely execute missions with spatial, temporal and reactive objectives expressed using Signal Temporal Logic. We formulate the problem of predictively avoiding collisions between two UAS without violating mission objectives as a Mixed Integer Linear Program (MILP).This however is intractable to solve online. Instead, we develop L2F, a two-stage collision avoidance method that consists of: 1) a learning-based decision-making scheme and 2) a distributed, linear programming-based UAS control algorithm. Through extensive simulations, we show the real-time applicability of our method which is $\approx\!6000\times$ faster than the MILP approach and can resolve $100\%$ of collisions when there is ample room to maneuver, and shows graceful degradation in performance otherwise. We also compare L2F to two other methods and demonstrate an implementation on quad-rotor robots.

eess.SY↗

arXiv2019

F1/10: An Open-Source Autonomous Cyber-Physical Platform

In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of safety and performance on real vehicles is costly and hazardous. The use of such vehicles is also outside the reach of most researchers and students. In this paper, we present F1/10: an open-source, affordable, and high-performance 1/10 scale autonomous vehicle testbed. The F1/10 testbed carries a full suite of sensors, perception, planning, control, and networking software stacks that are similar to full scale solutions. We demonstrate key examples of the research enabled by the F1/10 testbed, and how the platform can be used to augment research and education in autonomous systems, making autonomy more accessible.

cs.RO↗