Backdoors to Planning


  • Martin Kronegger Vienna University of Technology
  • Sebastian Ordyniak Masaryk University Brno
  • Andreas Pfandler Vienna University of Technology



planning, backdoors, theoretical analysis, parameterized complexity, causal graph


Backdoors measure the distance to tractable fragments and have become an important tool to find fixed-parameter tractable (fpt) algorithms. Despite their success, backdoors have not been used for planning, a central problem in AI that has a high computational complexity. In this work, we introduce two notions of backdoors building upon the causal graph. We analyze the complexity of finding a small backdoor (detection) and using the backdoor to solve the problem (evaluation) in the light of planning with (un)bounded plan length/domain of the variables. For each setting we present either an fpt-result or rule out the existence thereof by showing parameterized intractability. In three cases we achieve the most desirable outcome: detection and evaluation are fpt.




How to Cite

Kronegger, M., Ordyniak, S., & Pfandler, A. (2014). Backdoors to Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).