3 citations · 3 across the 16 of their papers we have counts for
10 papers · 1 filter
Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi…
Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms
Mahdi Al-Husseini, Kyle H. Wray, Mykel J. Kochenderfer
The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of…
Rao-Blackwellized POMDP Planning
Jiho Lee, Nisar R. Ahmed, Kyle H. Wray +1
Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief update…
Entropy-regularized Point-based Value Iteration
Harrison Delecki, Marcell Vazquez-Chanlatte, Esen Yel +4
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based…
Constrained Hierarchical Monte Carlo Belief-State Planning
Arec Jamgochian, Hugo Buurmeijer, Kyle H. Wray +2
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning…
Active teacher selection for reward learning
Rachel Freedman, Justin Svegliato, Kyle Wray +1
Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes f…
Multi-Objective Policy Gradients with Topological Constraints
Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer +1
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a c…
Improving Competence for Reliable Autonomy
Connor Basich, Justin Svegliato, Kyle Hollins Wray +2
Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios…
Learning to Optimize Autonomy in Competence-Aware Systems
Connor Basich, Justin Svegliato, Kyle Hollins Wray +3
Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows…
Planning in Stochastic Environments with Goal Uncertainty
Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda +1
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncert…