Faster Stackelberg Planning via Symbolic Search and Information Sharing

Authors

  • Álvaro Torralba Aalborg University
  • Patrick Speicher CISPA Helmholtz Center for Information Security
  • Robert Künnemann CISPA Helmholtz Center for Information Security
  • Marcel Steinmetz Saarland University
  • Jörg Hoffmann Saarland University

Keywords:

Deterministic Planning, Adversarial Agents

Abstract

Stackelberg 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.

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Published

2021-05-18

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

Issue

Section

AAAI Technical Track on Planning, Routing, and Scheduling