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Published in Proceedings of the AAAI Conference on Artificial Intelligence, 2021
A student abstract on learning-based algorithm selection for optimal multi-agent path finding.
Published in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021
A deep learning-based algorithm selector for multi-agent path finding using shortest-path embeddings.
Published in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022
A study of how betweenness centrality relates to empirical hardness in multi-agent path finding.
Published in IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS), 2023
A poster on the relationship between graph connectivity and empirical hardness in grid-based multi-agent pathfinding.
Published in International Conference on Automated Planning and Scheduling (ICAPS), 2024
A study of how map connectivity affects the empirical hardness of grid-based multi-agent pathfinding instances.
Published in Ph.D. Dissertation, University of Southern California, 2024
Ph.D. dissertation on empirical hardness, algorithm selection, and map connectivity in multi-agent pathfinding.
Published in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025
A position paper outlining research challenges and opportunities for studying empirical hardness in multi-agent pathfinding.
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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