Thayne T. Walker

According to our database1, Thayne T. Walker authored at least 15 papers between 2017 and 2026.

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Timeline

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Bibliography

2026
Draft-Conditioned Constrained Decoding for Structured Generation in LLMs.
CoRR, March, 2026

2025
Hierarchical Multi-agent Reinforcement Learning with Epistemic Priors for Scalable Communicationless Coordination of Teamable Agents.
Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Applications, 2025

2024
On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning.
CoRR, 2024

Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search.
Proceedings of the Seventeenth International Symposium on Combinatorial Search, 2024

2023
Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials.
IEEE Trans. Artif. Intell., December, 2023

Multi-Agent Reinforcement Learning with Epistemic Priors.
Proceedings of the 9th International Conference on Control, 2023

2021
Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications.
CoRR, 2021

Hierarchical Reinforcement Learning for Air-to-Air Combat.
CoRR, 2021

Conflict-Based Increasing Cost Search.
Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

2020
Generalized and Sub-Optimal Bipartite Constraints for Conflict-Based Search.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Tech Report: Efficient and Exact Collision Detection for Circular Agents.
CoRR, 2019

Unbounded Sub-Optimal Conflict-Based Search in Complex Domains.
Proceedings of the Twelfth International Symposium on Combinatorial Search, 2019

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks.
Proceedings of the Twelfth International Symposium on Combinatorial Search, 2019

2018
Extended Increasing Cost Tree Search for Non-Unit Cost Domains.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017
Using Hierarchical Constraints to Avoid Conflicts in Multi-Agent Pathfinding.
Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, 2017


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