Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Backdoors to Planning
Authors: Martin Kronegger, Sebastian Ordyniak, Andreas Pfandler
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we introduce two notions of backdoors building upon the causal graph. We analyze the complexity of ο¬nding 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. |
| Researcher Affiliation | Academia | Martin Kronegger Vienna University of Technology, Vienna, Austria EMAIL Sebastian Ordyniak Masaryk University, Brno, Czech Republic EMAIL Andreas Pfandler Vienna University of Technology, Vienna, Austria EMAIL |
| Pseudocode | No | The paper describes algorithms and proofs in narrative text but does not include any clearly labeled pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or state that code is available. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical complexity analysis and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |