Search Results for author: Anshu Saksena

Found 7 papers, 0 papers with code

Explanation through Reward Model Reconciliation using POMDP Tree Search

no code implementations1 May 2023 Benjamin D. Kraske, Anshu Saksena, Anna L. Buczak, Zachary N. Sunberg

As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success.

Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points

no code implementations14 Feb 2023 Jennifer Sleeman, David Chung, Chace Ashcraft, Jay Brett, Anand Gnanadesikan, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie-Aude Pradal, Renske Gelderloos, Caroline Tang, Anshu Saksena, Larry White

We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC).

Question Answering

Detecting Anomalous Swarming Agents with Graph Signal Processing

no code implementations17 Mar 2021 Kevin Schultz, Anshu Saksena, Elizabeth P. Reilly, Rahul Hingorani, Marisel Villafane-Delgado

Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems.

Towards Indirect Top-Down Road Transport Emissions Estimation

no code implementations16 Mar 2021 Ryan Mukherjee, Derek Rollend, Gordon Christie, Armin Hadzic, Sally Matson, Anshu Saksena, Marisa Hughes

In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of road transport emissions.

Graph Signal Processing for Infrastructure Resilience: Suitability and Future Directions

no code implementations21 Jul 2020 Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly, Grace M. Hwang, Anshu Saksena

We assess a number of power distribution systems with respect to metrics of signal structure and identify several correlates to system properties and further demonstrate how these metrics relate to performance of some GSP techniques.

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

no code implementations6 Nov 2018 Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan Gardner, Daniel Genin, Joshua Silbermann, Michael Owen, Mykel J. Kochenderfer

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars.

Autonomous Driving Collision Avoidance +2

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