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Description

This research will bridge the gap between cognitive science models and machine learning techniques, resulting in autonomous agents that are not only powerful and adaptive but also understandable and safe to deploy.

The development of a dedicated probabilistic programming language will empower researchers and developers to create sophisticated autonomous systems with greater ease and confidence.

Publication Date

8-2016

Publisher

Research Development Office

City

Provo

Keywords

autonomous agents, bayesian models, DNNs

Disciplines

Computer Sciences

Probabilistic Programming for Perceptually Driven Autonomous Agents

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