author    = {Martin Kronegger and Sebastian Ordyniak and Andreas Pfandler},
  title     = {Backdoors to Planning},
  booktitle = {Proc. of the 28th AAAI Conference on Artificial Intelligence (AAAI-14)},
  year      = {2014},
  pages     = {2300-2307},
  publisher = {AAAI Press},
  abstract  = {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.}