Search Results for author: Brett W. Israelsen

Found 7 papers, 1 papers with code

A Factor-Based Framework for Decision-Making Competency Self-Assessment

no code implementations22 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.

Decision Making Decision Making Under Uncertainty

Factorized Machine Self-Confidence for Decision-Making Agents

1 code implementation15 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.

Decision Making Decision Making Under Uncertainty

"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships

no code implementations8 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.

"I can assure you [$\ldots$] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships

no code implementations1 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.

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

no code implementations27 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 implementations13 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

Generalized Laguerre Reduction of the Volterra Kernel for Practical Identification of Nonlinear Dynamic Systems

no code implementations3 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.

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