41 citations · 105 across the 30 of their papers we have counts for
12 papers · 1 filter
Robustness Analysis of POMDP Policies to Observation Perturbations
Benjamin Kraske, Qi Heng Ho, Federico Rossi +2
Policies for Partially Observable Markov Decision Processes (POMDPs) are often designed using a nominal system model. In practice, this model can deviate from the true system durin…
Leveraging the Value of Information in POMDP Planning
Zakariya Laouar, Qi Heng Ho, Zachary Sunberg
Partially observable Markov decision processes (POMDPs) offer a principled formalism for planning under state and transition uncertainty. Despite advances made towards solving larg…
Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives
Qi Heng Ho, Martin S. Feather, Federico Rossi +2
Partially Observable Markov Decision Processes (POMDPs) are powerful models for sequential decision making under transition and observation uncertainties. This paper studies the ch…
Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies
Benjamin Kraske, Zakariya Laouar, Zachary Sunberg
As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption. Explainable Artificial Intelligence (XAI) aim…
Recursively-Constrained Partially Observable Markov Decision Processes
Qi Heng Ho, Tyler Becker, Benjamin Kraske +5
Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Process…
Explanation through Reward Model Reconciliation using POMDP Tree Search
Benjamin D. Kraske, Anshu Saksena, Anna L. Buczak +1
As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success. Ensurin…
Optimality Guarantees for Particle Belief Approximation of POMDPs
Michael H. Lim, Tyler J. Becker, Mykel J. Kochenderfer +2
Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to…
Compositional Learning-based Planning for Vision POMDPs
Sampada Deglurkar, Michael H. Lim, Johnathan Tucker +3
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty. However, most…
Bayesian Optimized Monte Carlo Planning
John Mern, Anil Yildiz, Zachary Sunberg +2
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening at…
Improving Automated Driving through POMDP Planning with Human Internal States
Zachary Sunberg, Mykel Kochenderfer
This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and eff…
Online algorithms for POMDPs with continuous state, action, and observation spaces
Zachary Sunberg, Mykel Kochenderfer
Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spac…
The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving
Zachary Sunberg, Christopher Ho, Mykel Kochenderfer
Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infe…