Faster Stackelberg Planning via Symbolic Search and Information Sharing
Keywords:Deterministic Planning, Adversarial Agents
AbstractStackelberg planning is a recent framework where a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures security-related (leader=defender, follower=attacker) as well as robustness-related (leader=adversarial event, follower=agent) scenarios. Solving Stackelberg planning tasks requires solving many related planning tasks at the follower level (in the worst case, one for every possible leader plan). Here we introduce new methods to tackle this source of complexity, through sharing information across follower tasks. Our evaluation shows that these methods can significantly reduce both the time needed to solve follower tasks and the number of follower tasks that need to be solved in the first place.
How to Cite
Torralba, Álvaro, Speicher, P., Künnemann, R., Steinmetz, M., & Hoffmann, J. (2021). Faster Stackelberg Planning via Symbolic Search and Information Sharing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11998-12006. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17425
AAAI Technical Track on Planning, Routing, and Scheduling