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On the Computational Complexity of Stackelberg Planning and Meta-Operator Verification: Technical Report (2403.17826v1)

Published 26 Mar 2024 in cs.AI

Abstract: Stackelberg planning is a recently introduced single-turn two-player adversarial planning model, where two players are acting in a joint classical planning task, the objective of the first player being hampering the second player from achieving its goal. This places the Stackelberg planning problem somewhere between classical planning and general combinatorial two-player games. But, where exactly? All investigations of Stackelberg planning so far focused on practical aspects. We close this gap by conducting the first theoretical complexity analysis of Stackelberg planning. We show that in general Stackelberg planning is actually no harder than classical planning. Under a polynomial plan-length restriction, however, Stackelberg planning is a level higher up in the polynomial complexity hierarchy, suggesting that compilations into classical planning come with a worst-case exponential plan-length increase. In attempts to identify tractable fragments, we further study its complexity under various planning task restrictions, showing that Stackelberg planning remains intractable where classical planning is not. We finally inspect the complexity of meta-operator verification, a problem that has been recently connected to Stackelberg planning.

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References (25)
  1. Computational Complexity: A Modern Approach. Princeton, Online Draft Version.
  2. Computational complexity of planning and approximate planning in the presence of incompleteness. Artificial Intelligence, 122(1-2): 241–267.
  3. Bonet, B. 2010. Conformant plans and beyond: Principles and complexity. Artificial Intelligence, 174(3-4): 245–269.
  4. Planning with Incomplete Information as Heuristic Search in Belief Space. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems, AIPS 2000, 52–61. AAAI.
  5. Bylander, T. 1994. The Computational Complexity of Propositional STRIPS Planning. Artificial Intelligence, 69(1): 165–204.
  6. Strong Planning in Non-Deterministic Domains Via Model Checking. In Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems, AIPS 1998, 36–43. AAAI.
  7. Pareto-optimal defenses for the web infrastructure: Theory and practice. ACM Transactions on Privacy and Security, 26(2): 1–36.
  8. Red-black planning: A new systematic approach to partial delete relaxation. Artif. Intell., 221: 73–114.
  9. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence, 2(3–4): 189–208.
  10. Computers and Intractability. A Guide to the Theory of NP-Completeness. W. H. Freeman.
  11. The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research (JAIR), 14: 2531–302.
  12. Immerman, N. 1988. Nondeterministic space is closed under complementation. SIAM Journal on computing, 17(5): 935–938.
  13. Tractable Plan Existence Does Not Imply Tractable Plan Generation. Ann. Math. Artif. Intell., 22(3-4): 281–296.
  14. Littman, M. L. 1997. Probabilistic Propositional Planning: Representations and Complexity. In Proceedings of the Fourteenth National Conference on Artificial Intelligence AAAI 97, 748–754. AAAI Press / The MIT Press.
  15. Can I Really Do That? Verification of Meta-Operators via Stackelberg Planning. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023). IJCAI.
  16. Rintanen, J. 1999. Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research, 10: 323–352.
  17. Rintanen, J. 2004. Complexity of Planning with Partial Observability. In Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS 2004), 345–354. AAAI Press.
  18. Lifted Stackelberg Planning. In Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS 2023). AAAI Press.
  19. Savitch, W. J. 1970. Relationships between nondeterministic and deterministic tape complexities. Journal of computer and system sciences, 4(2): 177–192.
  20. Stackelberg planning: Towards effective leader-follower state space search. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), 6286–6293. AAAI Press.
  21. Formally reasoning about the cost and efficacy of securing the email infrastructure. In 2018 IEEE European Symposium on Security and Privacy (EuroS&P), 77–91. IEEE.
  22. Provably Difficult Combinatorial Games. SIAM J. Comput., 8(2): 151–174.
  23. Szelepcsényi, R. 1987. The method of forcing for nondeterministic automata. Bulletin European Association for Theoretical Computer Science, 33: 96–100.
  24. Faster Stackelberg Planning via Symbolic Search and Information Sharing. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), 11998–12006. AAAI Press.
  25. Turner, H. 2002. Polynomial-length planning spans the polynomial hierarchy. In Logics in Artificial Intelligence: 8th European Conference (JELIA 2002), 111–124. Springer.

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