Secure Control under Partial Observability with Temporal Logic Constraints

Ramasubramanian, Bhaskar; Clark, Andrew; Bushnell, Linda; Poovendran, Radha

doi:10.23919/acc.2019.8814630

Cited by 12 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The value-iteration procedure and the varying defender FSC size described above is new to this work, along with more detailed examples. This differentiates the present paper from a preliminary version that appears in [1].…”

Section: A Contributionsmentioning

confidence: 76%

See 1 more Smart Citation

Secure Control in Partially Observable Environments to Satisfy LTL Specifications

Ramasubramanian¹,

Niu²,

Clark³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The goal is to generate a defender policy to maximize satisfaction of a given temporal logic specification under any adversary policy. The search for policies is limited to the space of finite state controllers, which leads to a tractable approach to determine policies. We relate the satisfaction of the specification to reaching (a subset of) recurrent states of a Markov chain. We present an algorithm to determine a set of defender and adversary finite state controllers of fixed sizes that will satisfy the temporal logic specification, and prove that it is sound. We then propose a value-iteration algorithm to maximize the probability of satisfying the temporal logic specification under finite state controllers of fixed sizes. Lastly, we extend this setting to the scenario where the size of the finite state controller of the defender can be increased to improve the satisfaction probability. We illustrate our approach with an example.

show abstract

Section: A Contributionsmentioning

confidence: 76%

“…We write η 0 η 1 • • • := η |= φ when a trace η ∈ (2 A P ) ω satisfies an LTL formula φ. Here, the superscript ω serves to indicate the potential infinite length of the word 1 Definition 2.1 (LTL Semantics): Let η i = η i η i+1 . .…”

Section: A Linear Temporal Logicmentioning

confidence: 99%

Secure Control in Partially Observable Environments to Satisfy LTL Specifications

Ramasubramanian¹,

Niu²,

Clark³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Linear temporal logic (LTL) is an extension of classical propositional logic where time, as the modality, is used to reason about properties that are true in future states along a path [42]. Temporal logic provides a powerful way to capture a variety of qualitative properties in reactive systems [43]- [45]. If p represents an atomic proposition from the set of propositions AP, then the following grammar generates LTL formulas ϕ over AP.…”

Section: B Linear Temporal Logic (Ltl)mentioning

confidence: 99%

Modeling Operator Performance in Human-in-the-Loop Autonomous Systems

Muhammad

2021

IEEE Access

View full text Add to dashboard Cite

With the increasing role of human-in-the-loop (HITL) based autonomous systems, researchers have made several attempts to understand how an operator's performance is affected by various parameters. In such systems, the performance of the operator directly influences the overall system performance. Although operator performance has been extensively studied at various psychological, behavioral, and physical levels, to the best of our knowledge there is a lack of literature addressing how a variety of operator's internal characteristics and external environmental factors affect the performance of the system for various mission objectives. This paper addresses this issue and proposes a probabilistic model checking based approach to assess the performance of an HITL-based autonomous system. We model the system as a Markov decision process and use probabilistic model checking to assess the impact of various operator and environment parameters on application-specific mission objectives. In addition to considering key operator characteristics in the fatigue model, the proposed method captures dynamic workload, task type, and the impact of various break policies on overall mission objectives. The model can be adapted to carry out system analysis at a higher level of abstraction for a variety of applications. The proposed method is applied to assess various scenarios in a case study from the literature. The results obtained using the proposed method can help a system designer evaluate the impact of various operator and environment characteristics to improve application-specific mission objectives. INDEX TERMSOperator modeling, semi-autonomous systems, probabilistic model checking, UAV.

show abstract

“…The satisfaction of an LTL formula (for a single agent) in a partially observable environment was presented in [43]. This was extended to the setting with an adversary, who also only had partial observation of the environment, and whose goal was to prevent the defender from satisfying the LTL formula in [44]. These treatments, however, did not account for the presence of timing constraints on the satisfaction of a temporal logic formula.…”

Section: Related Workmentioning

confidence: 99%

Control Synthesis for Cyber-Physical Systems to Satisfy Metric Interval Temporal Logic Objectives under Timing and Actuator Attacks

Niu,

Ramasubramanian,

Clark

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.

show abstract

Secure Control under Partial Observability with Temporal Logic Constraints

Cited by 12 publications

References 38 publications

Secure Control in Partially Observable Environments to Satisfy LTL Specifications

Secure Control in Partially Observable Environments to Satisfy LTL Specifications

Modeling Operator Performance in Human-in-the-Loop Autonomous Systems

Control Synthesis for Cyber-Physical Systems to Satisfy Metric Interval Temporal Logic Objectives under Timing and Actuator Attacks

Contact Info

Product

Resources

About