Polynomial Algorithms for the Synthesis of Bounded Nets

The so-called synthesis problem for nets, which consists in deciding whether a given graph is isomorphic to the case graph of some net, and then constructing the net, has been solved in the litterature for various types of nets, ranging from elementary nets to Petri nets. The common principle for the synthesis is the idea of regions in graphs, representing possible extensions of places in nets. However, no practical algorithm has been defined so far for the synthesis. We give here explicit algorithms solving in polynomial time the synthesis problem for bounded nets from regular languages or from finite automata

The Synthesis Problem for Elementary Net Systems is NP-Complete

The so-called synthesis problem consists in deciding for a class of nets whether a given graph is isomorphic to the case graph of some net and then constructing the net. This problem has been solved for various classes of nets, ranging from elementary nets to Petri nets. The general principle is to compute regions in the graph, i.e. subsets of nodes liable to represent extensions of places of an associated net. The naive method of synthesis which relies on this principle leads to exponential algorithms for an arbitrary class of nets. In an earlier study, we gave algorithms that solve the synthesis problem in polynomial time for the class of bounded Petri nets. We show here that in contrast the synthesis problem is indeed NP-complete for the class of elementary nets. This result is independent from the results of Kunihiko Hiraishi, showing that both problems of separation and inhibition by regions at a given node of the graph are NP-complete

Optimizing Performance of Continuous-Time Stochastic Systems using Timeout Synthesis

We consider parametric version of fixed-delay continuous-time Markov chains (or equivalently deterministic and stochastic Petri nets, DSPN) where fixed-delay transitions are specified by parameters, rather than concrete values. Our goal is to synthesize values of these parameters that, for a given cost function, minimise expected total cost incurred before reaching a given set of target states. We show that under mild assumptions, optimal values of parameters can be effectively approximated using translation to a Markov decision process (MDP) whose actions correspond to discretized values of these parameters

Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms

Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the $\varepsilon$-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presented. Our approach is based on reduction of the problem to finding long-run average optimal strategies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of parameter (i.e., action) space. Since the set of actions in the discretized semi-MDP can be very large, a straightforward approach based on explicit action-space construction fails to solve even simple instances of the problem. The presented algorithm uses an enhanced policy iteration on symbolic representations of the action space. The soundness of the algorithm is established for parametric ACTMCs with alarm-event distributions satisfying four mild assumptions that are shown to hold for uniform, Dirac and Weibull distributions in particular, but are satisfied for many other distributions as well. An experimental implementation shows that the symbolic technique substantially improves the efficiency of the synthesis algorithm and allows to solve instances of realistic size.Comment: This article is a full version of a paper accepted to the Conference on Quantitative Evaluation of SysTems (QEST) 201

Extension of PRISM by Synthesis of Optimal Timeouts in Fixed-Delay CTMC

We present a practically appealing extension of the probabilistic model checker PRISM rendering it to handle fixed-delay continuous-time Markov chains (fdCTMCs) with rewards, the equivalent formalism to the deterministic and stochastic Petri nets (DSPNs). fdCTMCs allow transitions with fixed-delays (or timeouts) on top of the traditional transitions with exponential rates. Our extension supports an evaluation of expected reward until reaching a given set of target states. The main contribution is that, considering the fixed-delays as parameters, we implemented a synthesis algorithm that computes the epsilon-optimal values of the fixed-delays minimizing the expected reward. We provide a performance evaluation of the synthesis on practical examples

The complexity of Petri net transformations

Bibliography: pages 124-127.This study investigates the complexity of various reduction and synthesis Petri net transformations. Transformations that preserve liveness and boundedness are considered. Liveness and boundedness are possibly the two most important properties in the analysis of Petri nets. Unfortunately, although decidable, determining such properties is intractable in the general Petri net. The thesis shows that the complexity of these properties imposes limitations on the power of any reduction transformations to solve the problems of liveness and boundedness. Reduction transformations and synthesis transformations from the literature are analysed from an algorithmic point of view and their complexity established. Many problems regarding the applicability of the transformations are shown to be intractable. For reduction transformations this confirms the limitations of such transformations on the general Petri net. The thesis suggests that synthesis transformations may enjoy better success than reduction transformations, and because of problems establishing suitable goals, synthesis transformations are best suited to interactive environments. The complexity of complete reducibility, by reduction transformation, of certain classes of Petri nets, as proposed in the literature, is also investigated in this thesis. It is concluded that these transformations are tractable and that reduction transformation theory can provide insight into the analysis of liveness and boundedness problems, particularly in subclasses of Petri nets