43 research outputs found
On the robustness of temporal properties for stochastic models
Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic Hybrid Automata (SHA) are powerful formalisms to model and to reason about the dynamics of biological systems, due to their ability to capture the stochasticity inherent in biological processes. A classical question in formal modelling with clear relevance to biological modelling is the model checking problem. i.e. calculate the probability that a behaviour, expressed for instance in terms of a certain temporal logic formula, may occur in a given stochastic process. However, one may not only be interested in the notion of satisfiability, but also in the capacity of a system to mantain a particular emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic noise, or by possible small changes in the model parameters. To address this issue, researchers from the verification community have recently proposed several notions of robustness for temporal logic providing suitable definitions of distance between a trajectory of a (deterministic) dynamical system and the boundaries of the set of trajectories satisfying the property of interest. The contributions of this paper are twofold. First, we extend the notion of robustness to stochastic systems, showing that this naturally leads to a distribution of robustness scores. By discussing two examples, we show how to approximate the distribution of the robustness score and its key indicators: the average robustness and the conditional average robustness. Secondly, we show how to combine these indicators with the satisfaction probability to address the system design problem, where the goal is to optimize some control parameters of a stochastic model in order to best maximize robustness of the desired specifications
A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems
We propose a technique to detect and generate patterns in a network of
locally interacting dynamical systems. Central to our approach is a novel
spatial superposition logic, whose semantics is defined over the quad-tree of a
partitioned image. We show that formulas in this logic can be efficiently
learned from positive and negative examples of several types of patterns. We
also demonstrate that pattern detection, which is implemented as a model
checking algorithm, performs very well for test data sets different from the
learning sets. We define a quantitative semantics for the logic and integrate
the model checking algorithm with particle swarm optimization in a
computational framework for synthesis of parameters leading to desired patterns
in reaction-diffusion systems
RTLola Cleared for Take-Off: Monitoring Autonomous Aircraft
The autonomous control of unmanned aircraft is a highly safety-critical
domain with great economic potential in a wide range of application areas,
including logistics, agriculture, civil engineering, and disaster recovery. We
report on the development of a dynamic monitoring framework for the DLR ARTIS
(Autonomous Rotorcraft Testbed for Intelligent Systems) family of unmanned
aircraft based on the formal specification language RTLola. RTLola is a
stream-based specification language for real-time properties. An RTLola
specification of hazardous situations and system failures is statically
analyzed in terms of consistency and resource usage and then automatically
translated into an FPGA-based monitor. Our approach leads to highly efficient,
parallelized monitors with formal guarantees on the noninterference of the
monitor with the normal operation of the autonomous system
Robustness Measures and Monitors for Time Window Temporal Logic
Temporal logics (TLs) have been widely used to formalize interpretable tasks
for cyber-physical systems. Time Window Temporal Logic (TWTL) has been recently
proposed as a specification language for dynamical systems. In particular, it
can easily express robotic tasks, and it allows for efficient, automata-based
verification and synthesis of control policies for such systems. In this paper,
we define two quantitative semantics for this logic, and two corresponding
monitoring algorithms, which allow for real-time quantification of satisfaction
of formulas by trajectories of discrete-time systems. We demonstrate the new
semantics and their runtime monitors on numerical examples.Comment: Submitted to the 62nd IEEE Conference on Decision and Control
(CDC2023
Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking
For exhaustive formal verification, industrial-scale cyber-physical systems
(CPSs) are often too large and complex, and lightweight alternatives (e.g.,
monitoring and testing) have attracted the attention of both industrial
practitioners and academic researchers. Falsification is one popular testing
method of CPSs utilizing stochastic optimization. In state-of-the-art
falsification methods, the result of the previous falsification trials is
discarded, and we always try to falsify without any prior knowledge. To
concisely memorize such prior information on the CPS model and exploit it, we
employ Black-box checking (BBC), which is a combination of automata learning
and model checking. Moreover, we enhance BBC using the robust semantics of STL
formulas, which is the essential gadget in falsification. Our experiment
results suggest that our robustness-guided BBC outperforms a state-of-the-art
falsification tool.Comment: Accepted to HSCC 202
Signal Temporal Logic Neural Predictive Control
Ensuring safety and meeting temporal specifications are critical challenges
for long-term robotic tasks. Signal temporal logic (STL) has been widely used
to systematically and rigorously specify these requirements. However,
traditional methods of finding the control policy under those STL requirements
are computationally complex and not scalable to high-dimensional or systems
with complex nonlinear dynamics. Reinforcement learning (RL) methods can learn
the policy to satisfy the STL specifications via hand-crafted or STL-inspired
rewards, but might encounter unexpected behaviors due to ambiguity and sparsity
in the reward. In this paper, we propose a method to directly learn a neural
network controller to satisfy the requirements specified in STL. Our controller
learns to roll out trajectories to maximize the STL robustness score in
training. In testing, similar to Model Predictive Control (MPC), the learned
controller predicts a trajectory within a planning horizon to ensure the
satisfaction of the STL requirement in deployment. A backup policy is designed
to ensure safety when our controller fails. Our approach can adapt to various
initial conditions and environmental parameters. We conduct experiments on six
tasks, where our method with the backup policy outperforms the classical
methods (MPC, STL-solver), model-free and model-based RL methods in STL
satisfaction rate, especially on tasks with complex STL specifications while
being 10X-100X faster than the classical methods.Comment: Accepted by IEEE Robotics and Automation Letters (RA-L) and ICRA202