Observable Discrete Event Research Articles

Verification of Pattern–Pattern Diagnosability in Partially Observed Discrete Event Systems

Verification of Pattern–Pattern Diagnosability in Partially Observed Discrete Event Systems

Design of Supervisors for Partially Observed Discrete Event Systems Using Quiescent Information

Design of Supervisors for Partially Observed Discrete Event Systems Using Quiescent Information

A State Space Approach to Decentralized Fault SE-Coprognosability of Partially Observed Discrete Event Systems.

The problem of fault prognosis in the context of discrete event systems (DESs) is a crucial subject to study the security and maintenance of cyber-physical systems. In this article, the decentralized fault prognosis of partially observed DESs is analyzed with a universal state-estimate-based protocol. It follows (M,K) as the performance bound of any expected decentralized prognosers, where any fault can be predicted K steps before its occurrence and the fault is guaranteed to occur within M steps once a corresponding fault alarm is issued. To determine whether expected decentralized prognosers exist, the notion of state-estimate-coprognosability (SE-coprognosability) under the case of one fault type is proposed. Compared with existing other kinds of coprognosability, SE-coprognosability is a more generalized concept. Meanwhile, combining the formal method and algebraic state space approach, a novel state estimation algorithm is presented and based on which, the verification of SE-coprognosability is also solved.

Matrix Approach for Verification of Opacity of Partially Observed Discrete Event Systems

Opacity is a confidential property characterizing whether the secret of a system can be inferred or not by an outside observer (also called an intruder). This paper focuses on presenting a matrix-based approach for verification of opacity of nondeterministic discrete event systems (DESs). Firstly, the given system is modeled as a finite-state automaton. Further, based on Boolean semi-tensor product (BSTP) of matrices, the algebraic expression of the observable dynamic of the system can be obtained. We, respectively, investigate current-state opacity and K-step opacity owing to the equivalence between a few opacity properties. Finally, necessary and sufficient conditions are presented to verify whether the secret is opaque for a given system, and the proposed methodology is tested effectively by examples. The matrix-based characterization of opacity proposed in this paper may provide a helpful angel for understanding the structure of this property.

On-Line Synthesis of Permissive Supervisors for Partially Observed Discrete Event Systems under scLTL Constraints

Abstract We consider a supervisory control problem of a discrete event system (DES) under partial observation, where a control specification is given by a fragment of linear temporal logic We design an on-line supervisor that dynamically computes its control action with the complete information of the product automaton of the DES and an acceptor for the specification. The concepts of controllability and observability are defined by means of a ranking function defined on the product automaton, which decreases its value if an accepting state of the produc automaton is being approached. The proposed on-line control scheme leverages the ranking function and a permissiveness function, which represents a time-varying permissiveness level As a result, the on-line supervisor achieves the specification, being aware of the tradeoff between its permissiveness and acceptance of the specification, if the product automaton is controllable and observable.

Optimizing Transportation Dynamics at a City-Scale Using a Reinforcement Learning Framework

Urban planners, authorities, and numerous additional players have to deal with challenges related to the rapid urbanization process and its effect on human mobility and transport dynamics. Hence, optimize transportation systems represents a unique occasion for municipalities. Indeed, the quality of transport is linked to economic growth, and by decreasing traffic congestion, the life quality of the inhabitants is drastically enhanced. Most state-of-the-art solutions optimize traffic in specific and small zones of cities (e.g., single intersections) and cannot be used to gather insights for an entire city. Moreover, evaluating such optimized policies in a realistic way that is convincing for policy-makers can be extremely expensive. In our work, we propose a reinforcement learning frameworks to overtake these two limitations. In particular, we use human mobility data to optimize the transport dynamics of three real-world cities (i.e., Berlin, Santiago de Chile, Dakar) and a synthesized one (i.e., SynthTown). To this end, we transform the transportation dynamics’ simulator MATSim into a realistic reinforcement learning environment able to optimize and evaluate transportation policies using agents that perform realistic daily activities and trips. In this way, we can assess transportation policies in a manner that is convincing for policy-makers. Finally, we develop a model-based reinforcement learning algorithm that approximates MATSim dynamics with a Partially Observable Discrete Event Decision Process (PODEDP) and, with respect to other state-of-art policy optimization techniques, can scale on big transportation data and find optimal policies also on a city-scale.

Partially observed discrete-event systems: from state estimation to intrusion detection

This paper summarizes the content of my plenary talk in the 14th Workshop on Discrete Event Systems (Wodes18) whose goal is that of describing the general setting of partially observable discrete event systems. Special attention is devoted to four problems, namely state estimation, fault diagnosis, opacity, and intrusion detection. It is shown, by means of simple and intuitive examples, how the above-mentioned problems can be formulated and addressed, either using finite state automata or Petri nets.

Petri-Net Controller Synthesis for Partially Controllable and Observable Discrete Event Systems

To enforce linear constraints on Petri nets that are partially controllable and observable, this work proposes an approach based on constraint transformation. First, a state-space equation of a Petri net control system based on event feedback is obtained by expressing a control action as a matrix, and the optimal control policy is designed. However, this policy needs to solve a nonlinear program on line. Second, pre-transition-gain-transformation is proposed to equivalently transform a constraint into a disjunction of new ones for an uncontrollable transition, and, similarly, post-transition-gain-transformation to transform a constraint into a disjunction of new ones for an unobservable transition. An algorithm is then given to transform a constraint into a disjunction of admissible ones, and, consequently, an efficient policy, which may not be optimal, can be designed. Third, in order to guarantee that the policy be both efficient and optimal, a dynamic linear constraint is introduced. Further, observing-transformation is proposed to simplify a dynamic constraint for an unobservable transition, and an algorithm is given to equivalently transform a class of linear constraints into admissible dynamic ones. As a result, an optimal controller requiring little online computation can be designed accordingly for some class of Petri nets. Finally, a maze system is used to illustrate the theoretical results.

Diagnosability Planning for Controllable Discrete Event Systems

In this paper, we propose an approach to ensure the diagnosability of a partially controllable system. Given a model of correct and faulty behaviors of a partially observable discrete event system, equipped with a set of elementary actions that do not intertwine with autonomous events, we search a diagnosability plan, i.e., a sequence of applicable actions that leads the system from an initial belief state (a set of potentially current states) to a diagnosable belief state, in which the system is then left to run freely. This helps in reducing the diagnosis interaction with running systems and can be applied, e.g., on the output of a repair plan, like in power networks. The two successive stages of this approach keep diagnosability planning, including diagnosability tests, in PSpace in comparison to the Exptime test for the more complex active diagnosability used usually in such cases. For this, we propose to construct incrementally the twin plant structure of the given system and to exploit its parts already constructed while testing the candidate plans and constructing its next parts. This helps in pruning the twin plant constructions and many non-diagnosability plan tests. We have created a special benchmark and tested three proposed methods, according to the recycling level of twin plants construction, with one cost function used for plan optimality and an optional heuristics.

Abstraction-Based Verification for Partially Observed Discrete Event Systems

Several properties including observability, normality, diagnosability, and prognosability (predictability) have been proposed for control/diagnosis of partially observed discrete event systems. Although these properties can be verified in polynomial time, their verification may be computationally infeasible for large-scale systems. To reduce the computational effort, abstraction-based verification of diagnosability has been studied in literature. The purpose of this paper is to complement this existing result by presenting conditions under which observability, normality, and prognosability can be verified based on the abstracted models. As a result, diagnosability and prognosability can be verified under the same conditions. Furthermore, observability and normality can be verified under weaker conditions.

Robust Failure Diagnosis of Partially Observed Discrete Event Systems

In this paper, we study robust failure diagnosis of partially observed discrete event systems. Given a set of possible models, each of which has its own nonfailure specification, the objective is to synthesize a single diagnoser such that, for all possible models, it detects any occurrence of a failure within a uniformly bounded number of steps. We call such a diagnoser a robust diagnoser. We introduce a notion of robust diagnosability, and prove that it serves as a necessary and sufficient condition for the existence of a robust diagnoser. We then present an algorithm for verifying the robust diagnosability condition. Moreover, we show that a robust diagnoser can be synthesized as an online diagnoser.

OFF-LINE IDENTIFICATION FOR A CLASS OF DISCRETE EVENT SYSTEMS USING SAFE PETRI NETS

This paper presents an identification method of partially observable Discrete Event System (DES) using safe Petri nets. This method allows constructing the formal model for a DES in a form of Petri net. The proposed method is based on the use of the input/output sequences constructed during the previous evolution of the system. The main advantage of this method is that priori knowledge are not needed and, a simple use of the measured Input/Output signals allows a complete identification of the system.

SUPERVISORY CONTROL IN DISCRETE EVENT SYSTEMS: EXISTENCE AND SOLUTIONS

Petri net controller synthesis is formally treated in this paper for Discrete Event Systems totally and partially observable for forbidden state problem and forbidden statetransition problem specifications. The existence conditions of controller for partially observable Discrete Event Systems are discussed. An alternative solution based on Marked Graph has been proposed for the case when the Petri net controller do not exists.

Maximizing Robustness of Supervisors for Partially Observed Discrete Event Systems

We consider a robust supervisory control problem to synthesize a supervisor for the nominal plant model which maximizes robustness. Cury and Krogh have first addressed this problem by unnecessarily restricting the upper bound of the legal behavior. In our previous work, we have solved the problem without restricting the upper bound of the legal behavior when the specification is described by prefix-closed languages. In this paper, we extend our previous work to the partial (event) observation case. First, we synthesize a supervisor that maximizes robustness. This result shows that robustness can be optimized under partial observation while permissiveness cannot be optimized in general. We next consider a special case where all controllable events are observable. In this special case, we synthesize a maximally permissive supervisor for the nominal plant model which maximizes not only robustness but also permissiveness for the maximal set of admissible plant variations.