Competency Assessment for Autonomous Agents using Deep Generative Models

March 23, 2022 ยท Declared Dead ยท ๐Ÿ› IEEE/RJS International Conference on Intelligent RObots and Systems

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Authors Aastha Acharya, Rebecca Russell, Nisar R. Ahmed arXiv ID 2203.12670 Category cs.LG: Machine Learning Cross-listed cs.AI, cs.HC, cs.NE, cs.RO Citations 13 Venue IEEE/RJS International Conference on Intelligent RObots and Systems Last Checked 4 months ago
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.
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