Quantitative Supervisory Control Game for Discrete Event Systems

Pruekprasert, Sasinee; Ushio, Toshimitsu; Kanazawa, Takafumi

doi:10.1109/tac.2015.2513901

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…Proof: By (2), 2) and 3) of Lemma 1, and 1) of Lemma 3, for any s ∈ W , there exists a directed controller SV d such that V SV d (s) = 0. Thus, by (7), we have V * (s) = 0 for any s ∈ W . Hence, by 1) of Lemma 3, for any optimal directed controller SV d * , (12) holds.…”

Section: B Properties Between Directed Controllers and Expected Returnmentioning

confidence: 96%

See 1 more Smart Citation

Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications

Oura¹,

Ushio²,

Sakakibara³

2021

Preprint

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In this paper, we consider supervisory control of stochastic discrete event systems (SDESs) under linear temporal logic specifications. Applying the bounded synthesis, we reduce the supervisor synthesis into a problem of satisfying a safety condition. First, we consider a synthesis problem of a directed controller using the safety condition. We assign a negative reward to the unsafe states and introduce an expected return with a statedependent discount factor. We compute a winning region and a directed controller with the maximum satisfaction probability using a dynamic programming method, where the expected return is used as a value function. Next, we construct a permissive supervisor via the optimal value function. We show that the supervisor accomplishes the maximum satisfaction probability and maximizes the reachable set within the winning region. Finally, for an unknown SDES, we propose a two-stage modelfree reinforcement learning method for efficient learning of the winning region and the directed controllers with the maximum satisfaction probability. We also demonstrate the effectiveness of the proposed method by simulation.

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Section: B Properties Between Directed Controllers and Expected Returnmentioning

confidence: 96%

“…Infinite games such as mean-payoff or safety games have been utilized to synthesize a supervisor that accomplishes specified objectives. In [5]- [7], they formulated some infinite horizon objectives as mean-payoff games. A safety game was used to symbolically synthesize a permissive supervisor for timed DESs in [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications

Oura¹,

Ushio²,

Sakakibara³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Apart from stabilization and liveness, we also consider quantitative properties of generated weight flows when events occur consecutively in G. Specifically, we discuss the stability of weight flows via local average weights (payoffs) per a limited number of transitions, which differs from the asymptotic mean weights in [9], [20]. Given a run r = x 1…”

Section: System Modelmentioning

confidence: 99%

“…The conventional framework of qualitative supervisory control is also extended to quantitative settings, where supervisors are designed to achieve some measures defined over states and transitions. This topic has drawn considerable attention and been investigated under various frameworks, see, e.g., [7], [8], [10], [16], [19], [20], [26] for some recent advances.…”

Section: Introductionmentioning

confidence: 99%

Supervisory Control for Stabilization under Multiple Local Average Payoff Constraints

Yin

Xiao

2021

2021 60th IEEE Conference on Decision and Control (CDC)

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“…In particular, parity games can be used for control synthesis of reactive systems under Linear Temporal Logic (LTL) specifications [9]. On the other hand, quantitative games such as meanpayoff games [10] have been adapted to quantitative control specifications [11], [12]. One of such specifications is the mean-payoff threshold problem for the average control-signal length of self-triggered controllers.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Self-triggered Control of Continuous-time Non-deterministic Systems without Stability Assumptions for 2-LTL Specifications

Pruekprasert¹,

Eberhart²,

Dubut³

2020

Preprint

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We propose a symbolic self-triggered controller synthesis procedure for non-deterministic continuous-time nonlinear systems without stability assumptions. The goal is to compute a controller that satisfies two objectives. The first objective is represented as a specification in a fragment of LTL, which we call 2-LTL. The second one is an energy objective, in the sense that control inputs are issued only when necessary, which saves energy. To this end, we first quantise the state and input spaces, and then translate the controller synthesis problem to the computation of a winning strategy in a meanpayoff parity game. We illustrate the feasibility of our method on the example of a navigating nonholonomic robot.

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Quantitative Supervisory Control Game for Discrete Event Systems

Cited by 15 publications

References 33 publications

Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications

Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems with LTL Specifications

Supervisory Control for Stabilization under Multiple Local Average Payoff Constraints

Symbolic Self-triggered Control of Continuous-time Non-deterministic Systems without Stability Assumptions for 2-LTL Specifications

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