no code implementations • 25 Feb 2024 • Matt-Heun Hong, Zachary N. Sunberg, Danielle Albers Szafir
In an evaluation with twelve scientists, we found that Cieran effectively modeled user preferences to rank colormaps and leveraged this model to create new quality designs.
no code implementations • 21 Oct 2023 • Zakariya Laouar, Rayan Mazouz, Tyler Becker, Qi Heng Ho, Zachary N. Sunberg
In this paper, we present a framework that synthesizes maximally safe control policies for Jump Markov Linear Systems subject to stochastic mode switches.
no code implementations • 15 Oct 2023 • Qi Heng Ho, Tyler Becker, Benjamin Kraske, Zakariya Laouar, Martin S. Feather, Federico Rossi, Morteza Lahijanian, Zachary N. Sunberg
Evaluations on a set of benchmark problems demonstrate the efficacy of our algorithm and show that policies for RC-POMDPs produce more desirable behaviors than policies for C-POMDPs.
no code implementations • 1 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.
no code implementations • 14 Apr 2023 • Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian
This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints.
1 code implementation • 10 Oct 2022 • Michael H. Lim, Tyler J. Becker, Mykel J. Kochenderfer, Claire J. Tomlin, Zachary N. Sunberg
Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces.
no code implementations • 8 Jul 2022 • Qi Heng Ho, Roland B. Ilyes, Zachary N. Sunberg, Morteza Lahijanian
This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications.
1 code implementation • 17 Dec 2021 • Sampada Deglurkar, Michael H. Lim, Johnathan Tucker, Zachary N. Sunberg, Aleksandra Faust, Claire J. Tomlin
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty.
1 code implementation • 18 Dec 2020 • Michael H. Lim, Claire J. Tomlin, Zachary N. Sunberg
This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs).
1 code implementation • 10 Oct 2019 • Michael H. Lim, Claire J. Tomlin, Zachary N. Sunberg
Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve.