Researchers at the Netherlands' Radboud University and Eindhoven University of Technology, and the Universities of Austin and California, Berkeley, have formulated a new artificial intelligence (AI) method for reasoning about uncertainty.
The uncertain partially observable Markov decision processes (uPOMDPs) are basically real-world models that calculate event probabilities, so AI can make better and safer decisions faster.
Radboud's Nils Jansen said the uPOMDP approach "allows us to take all our calculations and theoretical information and use it in the real world on a more consistent, regular basis."
He added that systems like autonomous cars could use this method to explain errors in more detail, in order to account for them when calculating. Said Jansen, "This means they have more specific examples of what could go wrong, and make better and more adequate adjustments to avoid those specific risks."
From Radboud University (Netherlands)
Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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