Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Wray, Kyle"'
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 multiple underway watercraft for patient exchange is complicated by pa
Externí odkaz:
http://arxiv.org/abs/2410.04523
Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief updates. Sequential Importance Resampling Particle Filters (SIRPF), also kno
Externí odkaz:
http://arxiv.org/abs/2409.16392
Ambulance exchange points are preidentified sites where patients are transferred between evacuation platforms while en route to enhanced medical care. We propose a new capability for maritime medical evacuation, which involves co-opting underway wate
Externí odkaz:
http://arxiv.org/abs/2408.13847
The integration of manned and unmanned aircraft can help improve wildfire response. Wildfire containment failures occur when resources available to first responders, who execute the initial stages of wildfire management referred to as the initial att
Externí odkaz:
http://arxiv.org/abs/2406.17189
Autor:
Delecki, Harrison, Vazquez-Chanlatte, Marcell, Yel, Esen, Wray, Kyle, Arnon, Tomer, Witwicki, Stefan, Kochenderfer, Mykel J.
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these types of uncertainty because they
Externí odkaz:
http://arxiv.org/abs/2402.09388
Autor:
Pettet, Ava, Zhang, Yunuo, Luo, Baiting, Wray, Kyle, Baier, Hendrik, Laszka, Aron, Dubey, Abhishek, Mukhopadhyay, Ayan
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by in
Externí odkaz:
http://arxiv.org/abs/2401.03197
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty. Unfortunately, online CPOMDP p
Externí odkaz:
http://arxiv.org/abs/2310.20054
Reinforcement learning from human feedback (RLHF) enables machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes from a single human teacher, despite querying a
Externí odkaz:
http://arxiv.org/abs/2310.15288
Autor:
Yildiz, Anil, Yel, Esen, Corso, Anthony L., Wray, Kyle H., Witwicki, Stefan J., Kochenderfer, Mykel J.
One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very costly and requires substantial data collection, it becomes computati
Externí odkaz:
http://arxiv.org/abs/2305.18633
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a curriculum, and enforcing measures of safety. A recently developed theo
Externí odkaz:
http://arxiv.org/abs/2209.07096