arXiv subjects

Kim G. Larsen

Publications and source records attributed to Kim G. Larsen.

At least 19 recordsLinked to original sources

arXiv2026

Computing Fixed Points using Dependency Oracles

We present global and local algorithms for solving systems of equations over Noetherian posets with a bottom element, a general setting underlying many verification problems. Our algorithms compute the solution of a selected variable by restricting exploration to those parts of the system required to determine its value. We achieve this by computing variable dependencies by means of dependency oracles. Oracles guide the exploration of the system and provide sound termination criteria for local fixed-point computation. A key advantage of our approach is its flexibility: oracles can be customized, composed, or over-approximated, offering a principled way to trade precision for performance without compromising correctness. We evaluate our solution against existing algorithms from the literature and show that our prototype implementation is competitive and often outperforms specialized solutions, while remaining simple and adaptable across diverse application domains.

cs.LO↗

arXiv2026

Exploiting Assumptions for Effective Monitoring of Real-Time Properties under Partial Observability

Runtime verification of temporal properties is essential for ensuring the correctness and reliability of real-time systems, particularly in cyber-physical systems. A significant challenge in this domain is the effective prediction of property failure or success, especially when dealing with partially observable systems. This paper addresses these challenges by developing an Assumption-Based Runtime Verification (ABRV) approach for a continuous real-time setting. Our method exploits assumptions about the system's behavior, specified as Timed Automata, to enable monitors to predict future outcomes and handle unobservable system parts, such as internal faults. Properties to be monitored are specified using Metric Interval Temporal Logic (MITL). The approach also includes formalizing observations with data and time uncertainty using sequences of timed constraints. We present a zone-based online algorithm for computing the monitoring verdict, implemented on top of the UPPAAL tool. Experimental evaluation on proof-of-concept cases demonstrates the approach's feasibility and effectiveness, illustrating how assumptions facilitate earlier verdicts, enable monitoring of properties dependent on unobservable events, and provide insights into scalability.

cs.FL↗

arXiv2026

A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data

Graph neural networks (GNNs) excel at predictive tasks on graph-structured data but often lack the ability to incorporate symbolic domain knowledge and perform general reasoning. Relational Bayesian Networks (RBNs), in contrast, enable fully generative probabilistic modeling over graph-like structures and support rich symbolic knowledge and probabilistic inference. This paper presents a neuro-symbolic framework that seamlessly integrates GNNs into RBNs, combining the learning strength of GNNs with the flexible reasoning capabilities of RBNs. We develop two implementations of this integration: one compiles GNNs directly into the native RBN language, while the other maintains the GNN as an external component. Both approaches preserve the semantics and computational properties of GNNs while fully aligning with the RBN modeling paradigm. We also propose a maximum a posteriori (MAP) inference method for these neuro-symbolic models. To demonstrate the framework's versatility, we apply it to two distinct problems. First, we transform a GNN for node classification into a collective classification model that explicitly models homo- and heterophilic label patterns, substantially improving accuracy on both synthetic and real-world datasets. Second, we introduce a multi-objective network optimization problem in environmental planning, where MAP inference and continuous numeric relaxation support a complex decision-making task. Both applications include new publicly available benchmark datasets. This work introduces a powerful and coherent neuro-symbolic approach to graph data, bridging learning and reasoning in ways that enable novel applications and improved performance across diverse tasks.

cs.AI↗

arXiv2026

TAPAAL SMC: Statistical Model Checking of Stochastic Timed-Arc Petri Nets

Timed-Arc Petri net (TAPN) is a timed extension of the classical Petri net model where tokens have their age and input arcs are associated with time intervals restricting the ages of tokens available for transition firing. Additionally, a TAPN can also contain place invariants constraining the ages of tokens in places, inhibitor arcs preventing a transition from firing and transport arcs that preserve token ages upon firing. This set of features, as much as it allows us to model complex systems, also often makes verification problems computationally hard or even undecidable. Moreover, in order to model real-life examples, additional stochastic aspects are often necessary to capture the desired behaviour. We suggest the first stochastic semantics for TAPNs and design and implement the quantitative and qualitative Statistical Model Checking (SMC) algorithms in the model checker TAPAAL. We argue for the semantic choices we made in the stochastic semantics and prove that the semantics is well-behaving. On a number of case studies we demonstrate the practical applicability of our modelling formalism and its SMC implementation.

cs.DC↗

arXiv2025

Data-Selective Online Battery Identification Using Extended Time Regular Expressions

In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehicle. We consider the case of a vehicle driving on/off a motorway and construct an Extended Time Regular Expression (ETRE) to detect data segments fitting these driving patterns. Simulation results indicate that by only using up to 10.71% of the data on average, the proposed method provides a low-bias and low-variance estimator under non-negligible current and voltage noise compared to other conventional estimation algorithms.

eess.SY↗

arXiv2025

Time for Timed Monitorability

Monitoring is an important part of the verification toolbox, in particular in situations where exhaustive verification using, e.g., model-checking is infeasible. The goal of online monitoring is to determine the satisfaction or violation of a specification during runtime, i.e., based on finite execution prefixes. However, not every specification is amenable to monitoring, e.g., properties for which no finite execution can witness satisfaction or violation. Monitorability is the question of whether a given specification is amenable to monitoring, and has been extensively studied in discrete time. Here, we study the monitorability problem for real-time properties expressed as Timed Automata. For specifications given by deterministic Timed Muller Automata, we prove decidability while we show that the problem is undecidable for specifications given by nondeterministic Timed Büchi automata. Furthermore, we refine monitorability to also determine bounds on the number of events as well as the time that must pass before monitoring the property may yield an informative verdict. We prove that for deterministic Timed Muller automata, such bounds can be effectively computed. In contrast we show that for nondeterministic Timed Büchi automata such bounds are not computable.

cs.FL↗

arXiv2025

Efficient Runtime Verification of Real-Time Systems under Parametric Communication Delays

Timed Büchi automata provide a very expressive formalism for expressing requirements of real-time systems. Online monitoring and active testing of embedded real-time systems can then be achieved by symbolic execution of such automata on the trace observed from the system. This direct construction however only is faithful if observation of the trace is immediate in the sense that the monitor (or test harness, respectively) can assign exact time stamps to the actions it observes, which is rarely true in practice due to the substantial and fluctuating parametric delays introduced by the circuitry connecting the observed system to its monitoring or testing device. We present purely zone-based online monitoring and testing algorithms, which handle such parametric delays exactly without recurrence to costly verification procedures for parametric timed automata. We have implemented our algorithms on top of the real-time model checking tool UPPAAL, and report on encouraging initial results.

cs.FL↗

arXiv2025

On-The-Fly Symbolic Algorithm for Timed ATL with Abstractions

Verification of real-time systems with multiple components controlled by multiple parties is a challenging task due to its computational complexity. We present an on-the-fly algorithm for verifying timed alternating-time temporal logic (TATL), a branching-time logic with quantifiers over outcomes that results from coalitions of players in such systems. We combine existing work on games and timed CTL verification in the abstract dependency graph (ADG) framework, which allows for easy creation of on-the-fly algorithms that only explore the state space as needed. In addition, we generalize the conventional inclusion check to the ADG framework which enables dynamic reductions of the dependency graph. Using the insights from the generalization, we present a novel abstraction that eliminates the need for inclusion checking altogether in our domain. We implement our algorithms in Uppaal and our experiments show that while inclusion checking considerably enhances performance, our abstraction provides even more significant improvements, almost two orders of magnitude faster than the naive method. In addition, we outperform Uppaal Tiga, which can verify only a strict subset of TATL. After implementing our new abstraction in Uppaal Tiga, we also improve its performance by almost an order of magnitude.

cs.LO↗

arXiv2025

You May Delay, but Time Will Not: Timed Games Under Delayed Control

Inspired by Martin Fränzle's persistent and influential work on capturing and handling delay inherent to cyber-physical systems in the formal verification of such systems, we study timed games where controllable actions do not take effect immediately, but only after some delay, i.e., they are scheduled for later execution. We show that solving such games is undecidable if an unbounded number of actions can be pending. On the other hand, we present a doubly-exponential time algorithm for games with a bound on the number of pending actions, based on a reduction to classical timed games. This makes timed games under delayed control with bounded schedules solvable with existing tools like UPPAAL.

cs.FL↗

arXiv2024

Forward and Backward Constrained Bisimulations for Quantum Circuits using Decision Diagrams

Efficient methods for the simulation of quantum circuits on classic computers are crucial for their analysis due to the exponential growth of the problem size with the number of qubits. Here we study lumping methods based on bisimulation, an established class of techniques that has been proven successful for (classic) stochastic and deterministic systems such as Markov chains and ordinary differential equations. Forward constrained bisimulation yields a lower-dimensional model which exactly preserves quantum measurements projected on a linear subspace of interest. Backward constrained bisimulation gives a reduction that is valid on a subspace containing the circuit input, from which the circuit result can be fully recovered. We provide an algorithm to compute the constraint bisimulations yielding coarsest reductions in both cases, using a duality result relating the two notions. As applications, we provide theoretical bounds on the size of the reduced state space for well-known quantum algorithms for search, optimization, and factorization. Using a prototype implementation, we report significant reductions on a set of benchmarks. In particular, we show that constrained bisimulation can boost decision-diagram-based quantum circuit simulation by several orders of magnitude, allowing thus for substantial synergy effects.

quant-ph↗

arXiv2023

CGAAL: Distributed On-The-Fly ATL Model Checker with Heuristics

We present CGAAL, our efficient on-the-fly model checker for alternating-time temporal logic (ATL) on concurrent game structures (CGS). We present how our tool encodes ATL as extended dependency graphs with negation edges and employs the distributed on-the-fly algorithm by Dalsgaard et al. Our tool offers multiple novel search strategies for the algorithm, including DHS which is inspired by PageRank and uses the in-degree of configurations as a heuristic, IHS which estimates instability of assignment values, and LPS which estimates the distance to a state satisfying the constituent property using linear programming. CGS are input using our modelling language LCGS, where composition and synchronisation are easily described. We prove the correctness of our encoding, and our experiments show that our tool CGAAL is often one to three orders of magnitude faster than the popular tool PRISM-games on case studies from PRISM's documentation and among case studies we have developed. In our evaluation, we also compare and evaluate our search strategies, and find that our custom search strategies are often significantly faster than the usual breadth-first and depth-first search strategies.

cs.LO↗

arXiv2023

Timed I/O Automata: It is never too late to complete your timed specification theory

A specification theory combines notions of specifications and implementations with a satisfaction relation, a refinement relation and a set of operators supporting stepwise design. We develop a complete specification framework for real-time systems using Timed I/O Automata as the specification formalism, with the semantics expressed in terms of Timed I/O Transition Systems. We provide constructs for refinement, consistency checking, logical and structural composition, and quotient of specifications -- all indispensable ingredients of a compositional design methodology. The theory is backed by rigorous proofs and is being implemented in the open-source tool ECDAR.

cs.FL↗

arXiv2023

Energy Consumption Optimization in Radio Access Networks (ECO-RAN)

In recent years, mobile network operators are showing interest in reducing energy consumption. Toward this goal, in cooperation with the Danish company 2Operate we have developed a stochastic simulation environment for mobile networks. Our simulator interacts with historical data from 2Operate and allow us to turn on and off network cells, replay traffic loads, etc. We have developed an optimization tool which is based on stochastic and distributed controllers computed by \uppaal. We have conducted experiments in our simulation tool. Experiments show that there is a potential to save up to 10\% of energy. We observe that for larger networks, there exists a larger potential for saving energy. Our simulator and \uppaal controllers, have been constructed in accordance to the 2Operate data and infrastructure. However, a main difference is that current equipment do not support updating schedulers on hourly bases. Nevertheless, new equipment e.g. new Huawei equipment do support changing schedulers on hourly basis. Therefore, integrating our solution in the production server of 2Operate is possible. However, rigorous testing in the production system is required.

cs.NI↗

arXiv2023

MM Algorithms to Estimate Parameters in Continuous-time Markov Chains

Continuous-time Markov chains (CTMCs) are popular modeling formalism that constitutes the underlying semantics for real-time probabilistic systems such as queuing networks, stochastic process algebras, and calculi for systems biology. Prism and Storm are popular model checking tools that provide a number of powerful analysis techniques for CTMCs. These tools accept models expressed as the parallel composition of a number of modules interacting with each other. The outcome of the analysis is strongly dependent on the parameter values used in the model which govern the timing and probability of events of the resulting CTMC. However, for some applications, parameter values have to be empirically estimated from partially-observable executions. In this work, we address the problem of estimating parameter values of CTMCs expressed as Prism models from a number of partially-observable executions. We introduce the class parametric CTMCs -- CTMCs where transition rates are polynomial functions over a set of parameters -- as an abstraction of CTMCs covering a large class of Prism models. Then, building on a theory of algorithms known by the initials MM, for minorization-maximization, we present iterative maximum likelihood estimation algorithms for parametric CTMCs covering two learning scenarios: when both state-labels and dwell times are observable, or just state-labels are. We conclude by illustrating the use of our technique in a simple but non-trivial case study: the analysis of the spread of COVID-19 in presence of lockdown countermeasures.

cs.LG↗

arXiv2023

Optimality-preserving Reduction of Chemical Reaction Networks

Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled nonlinear ordinary differential equations. In many applications, for example, in systems biology and epidemiology, CRN parameters such as the kinetic reaction rates can be used as control inputs to steer the system toward a given target. Unfortunately, the resulting optimal control problem is nonlinear, therefore, computationally very challenging. We address this issue by introducing an optimality-preserving reduction algorithm for CRNs. The algorithm partitions the original state variables into a reduced set of macro-variables for which one can define a reduced optimal control problem from which one can exactly recover the solution of the original control problem. Notably, the reduction algorithm runs with polynomial time complexity in the size of the CRN. We use this result to reduce reachability and control problems of large-scale protein-interaction networks and vaccination models with hundreds of thousands of state variables.

eess.SY↗

arXiv2021

A general framework for defining and optimizing robustness

Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. Therefore, in this paper, we propose a rigorous and flexible framework for defining different types of robustness properties for classifiers. Our robustness concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy, and that it should be defined in purely mathematical terms without reliance on algorithmic procedures for its measurement. We develop a very general robustness framework that is applicable to any type of classification model, and that encompasses relevant robustness concepts for investigations ranging from safety against adversarial attacks to transferability of models to new domains. For two prototypical, distinct robustness objectives we then propose new learning approaches based on neural network co-training strategies for obtaining image classifiers optimized for these respective objectives.

cs.LG↗

arXiv2021

Efficient Local Computation of Differential Bisimulations via Coupling and Up-to Methods

We introduce polynomial couplings, a generalization of probabilistic couplings, to develop an algorithm for the computation of equivalence relations which can be interpreted as a lifting of probabilistic bisimulation to polynomial differential equations, a ubiquitous model of dynamical systems across science and engineering. The algorithm enjoys polynomial time complexity and complements classical partition-refinement approaches because: (a) it implements a local exploration of the system, possibly yielding equivalences that do not necessarily involve the inspection of the whole system of differential equations; (b) it can be enhanced by up-to techniques; and (c) it allows the specification of pairs which ought not to be included in the output. Using a prototype, these advantages are demonstrated on case studies from systems biology for applications to model reduction and comparison. Notably, we report four orders of magnitude smaller runtimes than partition-refinement approaches when disproving equivalences between Markov chains.

cs.LO↗

arXiv2021

Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds

Storm water detention ponds are used to manage the discharge of rainfall runoff from urban areas to nearby streams. Their purpose is to reduce the hydraulic impact and sediment loads of the receiving waters. Detention ponds are currently designed based on static controls: the output flow of a pond is capped at a fixed value. This is not optimal with respect to the current infrastructure capacity and for some detention ponds it might even violate current regulations set by the European Water Framework Directive. We apply formal methods to synthesize (i.e., derive automatically) a safe and optimal active controller. We model the storm water detention pond, including the urban catchment area and the rain forecasts, as a hybrid Markov decision process. Subsequently, we use the tool Uppaal Stratego to synthesize a control strategy minimizing the cost related to pollution (optimality) while guaranteeing no emergency overflow of the detention pond (safety). Simulation results for an existing pond show that Uppaal Stratego can learn optimal strategies that prevent emergency overflows, where the current static control is not always able to prevent it. At the same time, our approach can improve sedimentation during low rain periods.

eess.SY↗