By Cyrille Artho, Axel Legay, Doron Peled
This e-book constitutes the complaints of the 14th overseas Symposium on automatic expertise for Verification and research, ATVA 2016, held in Chiba, Japan, in October 2016.
The 31 papers offered during this quantity have been conscientiously reviewed and chosen from eighty two submissions. They have been prepared in topical sections named: keynote; Markov versions, chains, and choice tactics; counter structures, automata; parallelism, concurrency; complexity, decidability; synthesis, refinement; optimization, heuristics, partial-order rate reductions; fixing systems, version checking; and software research.
Read Online or Download Automated Technology for Verification and Analysis: 14th International Symposium, ATVA 2016, Chiba, Japan, October 17-20, 2016, Proceedings PDF
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Extra resources for Automated Technology for Verification and Analysis: 14th International Symposium, ATVA 2016, Chiba, Japan, October 17-20, 2016, Proceedings
Various logics were developed for reasoning about mean payoﬀ and other reward-based properties . Model checking MDPs against speciﬁcations in these logics is supported by state of the art tools . MDPs with energy objectives have been studied in  as one-counter MDPs. g. ). A closely related paper  studies MDPs with combined energy-parity and mean-payoﬀparity objectives (however, the combination of energy with mean payoﬀ is not studied in ). A considerable amount of attention has been devoted to non-stochastic turnbased games with energy objectives [4,14].
1 (right). There is only one strategy (the empty function), and it is easy to verify that assigning 1/4 to each variable in LE solves the linear program with e∈T fe · E(e) = 14 . However, for every m there is a positive probability that the decrementing loop on t is taken at least m times, and thus the strategy is not safe. Fig. 1. A linear program LE with non-negative variables fe , e ∈ T (left), and an EMDP where the strategy corresponding to the solution of LE is not safe (right). Each transition is labelled by the associated counter update (in boldface), reward, and probability Although the program LE does not directly yield a safe strategy optimizing the mean payoﬀ, it is still useful for obtaining certain “building blocks” of such a strategy.
We are interested in assessing the sub-optimality of the policy obtained upon convergence of the approximate policy iteration scheme. Theorem 2 (Bounds on Sub-optimality of Approximate Policy Iteration). Assume that after a finite number of steps p a steady-state policy μp is obtained. ,p J˜mk (s) − Jμ (s) . We obtain k k the following sub-optimality bound: 2γδ J˜pmp (s) − J ∗ (s) ≤ , 1−γ m m m m where J˜p p (s) is obtained from J¯p p , and where J¯p p = T¯μpp J¯p . Proof. It follows from a straightforward adaptation of the results in [4, Section 3].