On-Line Monitoring for Temporal Logic Robustness

Dokhanchi, Adel; Hoxha, Bardh; Fainekos, Georgios

doi:10.1007/978-3-319-11164-3_19

Cited by 108 publications

(94 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, STL formulae have concrete meanings that are easy to be interpreted by humans with basic knowledge of temporal logic (not necessarily domain experts). Further, considering STL formulae as models, it is straightforward to construct monitors for run-time verification for potentially complex systems [8].…”

Section: Introductionmentioning

confidence: 99%

“…By maximizing the gap , optimization (8) attempts to maximize the separation between normal and anomalous outputs. By minimizing the function d(φ θ ), optimization (8) prevents the learned formula from trivially describing all observed signals (i.e., finding a formula such that L(φ) = L(S)), which would render the optimization redundant.…”

mentioning

confidence: 99%

“…2) ParameterEstimation solves (8) instead of (5). The ParameterEstimation procedure uses the heuristic tightness function d when calculating the objective function in (8). In this paper, we use a heuristic that penalizes the size of τ 1 , as for monitoring purposes we would prefer to infer formulae that can describe behaviors of the early parts of the system's outputs.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Temporal Logics for Learning and Detection of Anomalous Behavior

Kong

Jones

Belta

2017

IEEE Trans. Automat. Contr.

101

View full text Add to dashboard Cite

Abstract-The increased complexity of modern systems necessitates automated anomaly detection methods to detect possible anomalous behavior determined by malfunctions or external attacks. We present formal methods for inferring (via supervised learning) and detecting (via unsupervised learning) anomalous behavior. Our procedures use data to construct a signal temporal logic (STL) formula that describes normal system behavior. This logic can be used to formulate properties such as "If the train brakes within 500 m of the platform at a speed of 50 km/hr, then it will stop in at least 30 s and at most 50 s." Our procedure infers not only the physical parameters involved in the formula (e.g., 500 m in the example above) but also its logical structure. STL gives a more human-readable representation of behavior than classifiers represented as surfaces in high-dimensional feature spaces. The learned formula enables us to perform early detection by using monitoring techniques and anomaly mitigation by using formal synthesis techniques. We demonstrate the power of our methods with examples of naval surveillance and a train braking system.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Temporal Logics for Learning and Detection of Anomalous Behavior

Kong

Jones

Belta

2017

IEEE Trans. Automat. Contr.

101

View full text Add to dashboard Cite

show abstract

“…Beyond offline monitoring, it is also necessary to perform online monitoring of deployed artificial pancreas control systems to detect failures, caused due to rare events that may be hard to observe in clinical trials. In fact, the idea of robustness of a trace with respect to a logical specification can also be used to perform online monitoring for detecting potential failures early [21,23].…”

Section: Introduction: Artificial Pancreasmentioning

confidence: 99%

Towards a Verified Artificial Pancreas: Challenges and Solutions for Runtime Verification

Cameron

Fainekos

Maahs

et al. 2015

Runtime Verification

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper, we briefly examine the recent developments in artificial pancreas controllers, that automate the delivery of insulin to patients with type-1 diabetes. We argue the need for offline and online runtime verification for these devices, and discuss challenges that make verification hard. Next, we examine a promising simulation-based falsification approach based on robustness semantics of temporal logics. These ideas are implemented in the tool S-Taliro that automatically searches for violations of metric temporal logic (MTL) requirements for Simulink(tm)/Stateflow(tm) models. We illustrate the use of S-Taliro for finding interesting property violations in a PID-based hybrid closed loop control system.

show abstract

“…To evaluate whether such a formula ϕ holds on a given trajectory, only a finite-length prefix of that trajectory is needed. Its length can be upper-bounded by the horizon of ϕ, hrz(ϕ) ∈ N, calculable as shown in [16]. For example, the horizon of [0,2] (♦ [2,4] p) is 2+4=6.…”

Section: A Metric Temporal Logic (Mtl)mentioning

confidence: 99%

Smooth operator: Control using the smooth robustness of temporal logic

Pant

Abbas

Mangharam

2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

111

View full text Add to dashboard Cite

Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outper-forms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems. Abstract-Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outperforms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems. Keywords

show abstract

On-Line Monitoring for Temporal Logic Robustness

Cited by 108 publications

References 28 publications

Temporal Logics for Learning and Detection of Anomalous Behavior

Temporal Logics for Learning and Detection of Anomalous Behavior

Towards a Verified Artificial Pancreas: Challenges and Solutions for Runtime Verification

Smooth operator: Control using the smooth robustness of temporal logic

Contact Info

Product

Resources

About