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By Cyrille Artho, Axel Legay, Doron Peled

This booklet constitutes the complaints of the 14th foreign Symposium on automatic know-how for Verification and research, ATVA 2016, held in Chiba, Japan, in October 2016.

The 31 papers offered during this quantity have been rigorously reviewed and chosen from eighty two submissions. They have been prepared in topical sections named: keynote; Markov versions, chains, and selection procedures; counter platforms, automata; parallelism, concurrency; complexity, decidability; synthesis, refinement; optimization, heuristics, partial-order rate reductions; fixing tactics, version checking; and software research.

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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

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LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011). 1007/978-3-642-22110-1 47 13. : Formal verification and synthesis for discrete-time stochastic systems. IEEE Trans. Autom. Control 60(8), 2031–2045 (2015) 14. : Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees. In: Proceedings of the ICML, pp. 569–576. ACM (2005) 15. : Finite time bounds for fitted value iteration. J. Mach. Learn. Res. 9, 815–857 (2008) 16. : Markov Decision Processes: Discrete Stochastic Dynamic Programming.

E. it ensures that the energy level does not have a tendency to decrease. Intuitively, the optimal value of LE is the maximal expected mean payoff achievable under the constraint that the long-run average change (or trend ) of the energy level is non-negative. Every safe strategy has to satisfy this constraint, because otherwise the probability of visiting a configuration with negative counter would be positive. Thus, using the methods adopted from [6], we get the following. Optimizing the Expected Mean Payoff in Energy Markov Decision Processes 41 Lemma 4.

The current implementation of the aggregation strategy uses the average value of the reward function to adequately handle different shapes of the function. Note that for stiff functions we could additionally tune the aggregation strategy in order to provide better reduction and speedup, by states that are associated with small rewards (away from the maximum of the function) in large clusters. 2 Convergence of the Approximate Scheme We consider a different variant of the case study (named Robotic 2 ), where for each action there is an 80 % probability that the robot does not move, a 15 % probability that the action has the intended effect, and the remaining probability is uniformly distributed over the four undesired outputs, similarly as in the previous variant of the model.

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