no code implementations • 22 Mar 2022 • Brett W. Israelsen, Nisar Ahmed
We summarize our efforts to date in developing a framework for generating succinct human-understandable competency self-assessments in terms of machine self confidence, i. e. a robot's self-trust in its functional abilities to accomplish assigned tasks.
1 code implementation • 15 Oct 2018 • Brett W. Israelsen, Nisar R. Ahmed, Eric Frew, Dale Lawrence, Brian Argrow
Markov decision processes underlie much of the theory of reinforcement learning, and are commonly used for planning and decision making under uncertainty in robotics and autonomous systems.
no code implementations • 8 Nov 2017 • Brett W. Israelsen, Nisar R. Ahmed
People who design, use, and are affected by autonomous artificially intelligent agents want to be able to \emph{trust} such agents -- that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately.
no code implementations • 1 Aug 2017 • Brett W. Israelsen
As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately.
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.
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.
no code implementations • 3 Oct 2014 • Brett W. Israelsen, Dale A. Smith
In order to reduce the number of required coefficients, Laguerre polynomials are used to estimate the Volterra kernels.