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Found 16 papers, 3 papers with code
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e. g., accuracy in object detection and frames processing rate) with energy consumption.
Deductive Controller Synthesis for Probabilistic Hyperproperties
Our specification language builds on top of the logic HyperPCTL and enhances it with structural constraints over the synthesized controllers.
DeepSTL -- From English Requirements to Signal Temporal Logic
In this paper we propose DeepSTL, a tool and technique for the translation of informal requirements, given as free English sentences, into Signal Temporal Logic (STL), a formal specification language for cyber-physical systems, used both by academia and advanced research labs in industry.
Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications
We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications.
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets.
Analysis of Bayesian Networks via Prob-Solvable Loops
Prob-solvable loops are probabilistic programs with polynomial assignments over random variables and parametrised distributions, for which the full automation of moment-based invariant generation is decidable.
Control from Signal Temporal Logic Specifications with Smooth Cumulative Quantitative Semantics
We present a framework to synthesize control policies for nonlinear dynamical systems from complex temporal constraints specified in a rich temporal logic called Signal Temporal Logic (STL).
Systems and Control
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
Furthermore, this paper presents the main challenges in building a resilient IoT for CPS which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles).
A Robust Genetic Algorithm for Learning Temporal Specifications from Data
We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system.
ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans
Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state.
Temporal Logic as Filtering
We also provide a quantitative semantics for MTL, which measures the normalized, maximum number of times a formula is satisfied within its associated kernel, by a given signal.
Logic in Computer Science 03B44 F.4.1; D.3.1
Deep Neural Programs for Adaptive Control in Cyber-Physical Systems
We introduce Deep Neural Programs (DNP), a novel programming paradigm for writing adaptive controllers for cy-ber-physical systems (CPS).
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.
Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias
We present a novel approach to learn the formulae characterising the emergent behaviour of a dynamical system from system observations.
On the Robustness of Temporal Properties for Stochastic Models
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.
Monitoring with uncertainty
We discuss the problem of runtime verification of an instrumented program that misses to emit and to monitor some events.
