Competency Assessment for Autonomous Agents using Deep Generative Models

TL;DR

This paper introduces a probabilistic deep generative modeling approach for autonomous agents to reliably communicate their competency by simulating trajectories and calculating outcome probabilities.

Contribution

It develops a novel deep generative world model combining variational autoencoders and recurrent neural networks for long-term trajectory forecasting and competency assessment.

Findings

Accurately forecasts agent trajectories over long horizons.

Calculates precise outcome probability distributions.

Enables reliable competency communication for autonomous agents.

Abstract

For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform. Towards this objective, we develop probabilistic world models based on deep generative modelling that allow for the simulation of agent trajectories and accurate calculation of tasking outcome probabilities. By combining the strengths of conditional variational autoencoders with recurrent neural networks, the deep generative world model can probabilistically forecast trajectories over long horizons to task completion. We show how these forecasted trajectories can be used to calculate outcome probability distributions, which enable the precise assessment of agent competency for specific tasks and initial settings.