Backdoors to Planning
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Proceedings |
| Conference | AAAI Press |
| MU Faculty or unit | |
| Citation | |
| web | https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8376 |
| Field | Informatics |
| Keywords | parameterized complexity; planning; backdoors; causal graph |
| Description | 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. |
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