Adaptive Symbolic Control for Finite-State Transition Systems With Grammatical Inference

Fu, Jie; Tanner, Herbert G.; Heinz, Jeffrey; Chandlee, Jane

doi:10.1109/tac.2013.2272885

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Mizoguchi et al [26] design a feedback controller of a cyber-physical system by composing several abstract systems, and prove that the controlled system exhibits the desired behaviour. Fu et al [11] model adaptive control for finite-state transition systems using elements from grammatical inference and game theory, to produce controllers that guarantee that a system satisfies its specifications.…”

Section: Supervisory Controlmentioning

confidence: 99%

Generalised Dining Philosophers as Feedback Control

Choppella

Sanjeev

Kasturi

et al. 2019

Distributed Computing and Internet Technology

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We revisit the Generalised Dining Philosophers problem through the perspective of feedback control. The result is a modular development of the solution using the notions of system and system composition (the latter due to Tabuada) in a formal setting that employs simple equational reasoning. The modular approach separates the solution architecture from the algorithmic minutiae and has the benefit of simplifying the design and correctness proofs.Three variants of the problem are considered: N=1, and N > 1 with centralised and distributed topology. The base case (N=1) reveals important insights into the problem specification and the architecture of the solution. In each case, solving the Generalised Dining Philosophers reduces to designing an appropriate feedback controller.

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Section: Supervisory Controlmentioning

confidence: 99%

Generalised Dining Philosophers as Feedback Control

Choppella

Sanjeev

Kasturi

et al. 2019

Distributed Computing and Internet Technology

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“…This approach is orthogonal to active automata learning (AAL) such as L * Angluin's algorithm [3] and its recent variants [15,25]. AAL is suitable to capture the behaviours of black-box reactive systems and it has been successfully employed in the field of CPS to learn how to interact with the surrounding environments [10,13]. Mining temporal logic requirements has the following advantages with respect to AAL.…”

Section: Related Workmentioning

confidence: 99%

A Robust Genetic Algorithm for Learning Temporal Specifications from Data

Nenzi

Silvetti

Bartocci

et al. 2018

Lecture Notes in Computer Science

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We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [9] and with a recently proposed decision-tree [8] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work.

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“…In [25], Q-learning was applied to control MDPs from signal temporal logic (STL) specifications, where the reward was the STL robustness score -a measure of distance to satisfaction. Other closely related works include [26], [27], where the problem of LTL control was modeled as a game between a player (controller) and an adversary (environment). The controller inferred the "grammar" of actions taken by the environment.…”

Section: Introductionmentioning

confidence: 99%

Formal methods for adaptive control of dynamical systems

Sadraddini

Belta

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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We develop a method to control discrete-time systems with constant but initially unknown parameters from linear temporal logic (LTL) specifications. We introduce the notions of (non-deterministic) parametric and adaptive transition systems and show how to use tools from formal methods to compute adaptive control strategies for finite systems. For infinite systems, we first compute abstractions in the form of parametric finite quotient transition systems and then apply the techniques for finite systems. Unlike traditional adaptive control methods, our approach is correct by design, does not require a reference model, and can deal with a much wider range of systems and specifications. Illustrative case studies are included.• X is a (possibly infinite) set of states; • U is a (possibly infinite) set of control inputs;

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Adaptive Symbolic Control for Finite-State Transition Systems With Grammatical Inference

Cited by 9 publications

References 19 publications

Generalised Dining Philosophers as Feedback Control

Generalised Dining Philosophers as Feedback Control

A Robust Genetic Algorithm for Learning Temporal Specifications from Data

Formal methods for adaptive control of dynamical systems

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