Artificial Intelligence


Multi-Scalar Multi-Agent Control for Optimization of Dynamic Networks Operating in Remote Environment

Authors: Martin Dudziak

Multi-agent control systems have demonstrated effectiveness in a variety of physical applications including cooperative robot networks and multi-target tracking in high-noise network and group environments. We introduce the use of multi-scalar models that extend cellular automaton regional neighborhood comparisons and local voting measures based upon stochastic approximation in order to provide more efficient and time-sensitive solutions to non-deterministic problems. The scaling factors may be spatial, temporal or in other semantic values. The exercising of both cooperative and competitive functions by the devices in such networks offers a method for optimizing system parameters to reduce search, sorting, ranking and anomaly evaluation tasks. Applications are illustration for a group of robots assigned different tasks in remote operating environments with highly constrained communications and critical fail-safe conditions.

Comments: 7 Pages.

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Submission history

[v1] 2017-11-30 02:13:24

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