Patent attributes
The present disclosure relates to the intelligent distribution of data for robotic, autonomous, and similar systems. To reduce the impact of multi-agent coordination on networked systems embodiments are disclosed that include the use of action-based constraints which yield constrained-action POMDP (CA-POMDP) models, and probabilistic constraint satisfaction for the resulting infinite-horizon finite state controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite state controller (FSC). A combination of a Markov chain Monte Carlo (MCMC) routine and a discrete optimization routine can be performed on the finite state controller to improve probabilistic constraint satisfaction of the finite state controller, while minimizing impact to a value function.
