Application and Theory of Petri Nets and Concurrency: 35th by Gianfranco Ciardo, Ekkart Kindler

By Gianfranco Ciardo, Ekkart Kindler

This publication constitutes the complaints of the thirty fifth foreign convention on program and conception of Petri Nets and Concurrency, PETRI NETS 2014, held in Tunis, Tunisia, in June 2014. The 15 average papers and four software papers awarded during this quantity have been rigorously reviewed and chosen from forty eight submissions. additionally the booklet comprises three invited talks in complete paper size. The papers conceal numerous subject matters within the box of Petri nets and similar types of concurrency.

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Sample text

We illustrate how rewardbounded reachability properties can be used to serve as increasing or decreasing path properties. For instance, when a is an atomic proposition or a Boolean r a are increasing, while combination thereof, the path properties ♦ r a and r r 1 their duals ♦ a and a are decreasing. Within these notations, the formula φe,u above can be reformulated as ♦ e (goal ∧ (utility u)) when the consumed energy is modeled by a reward function and the accumulated utility is assumed to be encoded in the states, or as ♦ u (goal ∧ (energy e)) when utility is represented by a reward function and the consumed energy is augmented to states.

P (si , si+1 ) > 0 for all i 0. The length |π| denotes the number of transitions taken in π. We refer to the value Energy-Utility Analysis for Resilient Systems Using PMC prob(π) = 25 P (si , si+1 ) 0 i

An infinite trajectory ϑ˜ = s0 t0 s1 t1 s2 t2 . . satisfies ϕ[r] if there is some n ∈ N such that rew (s0 t0 . . tn−1 sn ) e and sn is a stable state. Given some probability bound p and a non-stable state s in C with PCs (♦goal ) > p, a simple approximation scheme can be applied to compute es = inf e ∈ R : PCs (♦ e goal ) p [7]. The first step is an exponential search to i determine the smallest i ∈ N such that PCs ♦ 2 goal p. For this, we might use known algorithms for computing reward-bounded reachability probabilities in continuous-time Markov chains [4].

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