Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization

no code implementations • 27 Mar 2017 • Brett W. Israelsen, Nisar Ahmed, Kenneth Center, Roderick Green, Winston Bennett Jr

This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements.

Bayesian Optimization Decision Making

Towards Adaptive Training of Agent-based Sparring Partners for Fighter Pilots

no code implementations • 13 Dec 2016 • Brett W. Israelsen, Nisar Ahmed, Kenneth Center, Roderick Green, Winston Bennett Jr

One key benefit is that during optimization, the Gaussian Process learns a global estimate of the true objective function, with predicted outcomes and a statistical measure of confidence in areas that haven't been investigated yet.

Bayesian Optimization