Contributions to Proceedings:

A. Lukina, A. Tiwari, S. Smolka, R. Grosu:
"Distributed adaptive-neighborhood control for stochastic reachability in multi-agent systems";
in: "SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing", Association for Computing Machinery, New YorkNYUnited States, 2019, ISBN: 978-1-4503-5933-7, ##.

English abstract:
We present
, a distributed, adaptive-horizon and adaptive-
neighborhood algorithm for solving the stochastic reachability prob-
lem in multi-agent systems, in particular, ocking modeled as a
Markov decision process. At each time step, every agent rst calls
a centralized, adaptive-horizon model-predictive control (AMPC)
algorithm to obtain an optimal solution for its local neighborhood.
Second, the agents derive the ock-wide optimal solution through
a sequence of consensus rounds. Third, the neighborhood is adap-
tively resized using a ock-wide cost-based Lyapunov function.
This way
improves eciency without compromising con-
vergence. We evaluate
īs performance using statistical model
checking. Our results demonstrate that, compared to AMPC,
achieves considerable speed-up (two-fold in some cases) with only
a slightly lower rate of convergence. The smaller average neighbor-
hood size and lookahead horizon demonstrate the benets of the
approach for stochastic reachability problems involving any
controllable multi-agent system that possesses a cost function.

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