activity
20212024
most citedCOA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations

2 citations · 6 across the 8 of their papers we have counts for

collaborators

8 papers

cs.HC2024

Re-Envisioning Command and Control

Kaleb McDowell, Ellen Novoseller, Anna Madison +2

Future warfare will require Command and Control (C2) decision-making to occur in more complex, fast-paced, ill-structured, and demanding conditions. C2 will be further complicated…

cs.LG2024

Scalable Interactive Machine Learning for Future Command and Control

Anna Madison, Ellen Novoseller, Vinicius G. Goecks +7

Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robus…

cs.AI20242 cited

COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations

Vinicius G. Goecks, Nicholas Waytowich

The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process. Addressing this challenge, this study introduces COA-GPT…

cs.LG20231 cited

DIP-RL: Demonstration-Inferred Preference Learning in Minecraft

Ellen Novoseller, Vinicius G. Goecks, David Watkins +2

In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in…

cs.RO2023

Learning Flight Control Systems from Human Demonstrations and Real-Time Uncertainty-Informed Interventions

Prashant Ganesh, J. Humberto Ramos, Vinicius G. Goecks +4

This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models…

cs.AI20231 cited

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27

To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…