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..
Variable-Deletion Backdoors to Planning
Authors: Martin Kronegger, Sebastian Ordyniak, Andreas Pfandler
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we improve the situation by defining a new type of variabledeletion backdoors based on the extended causal graph of a planning instance. For this notion of backdoors several fixed-parameter tractable algorithms are identified. Furthermore, we explore the capabilities of polynomial time preprocessing, i.e., we check whether there exists a polynomial kernel. Our results also show the close connection between planning and verification problems such as Vector Addition System with States (VASS). |
| Researcher Affiliation | Academia | 1Vienna University of Technology, Vienna, Austria 2Masaryk University, Brno, Czech Republic 3University of Siegen, Siegen, Germany |
| Pseudocode | No | The paper describes procedures and constructions (e.g., for VASS) in text, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focused on algorithms and complexity analysis. It does not use or reference any datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve experimental validation or dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | This is a theoretical paper. No specific software dependencies with version numbers are mentioned that would be needed to replicate experimental results or implementations. |
| Experiment Setup | No | This is a theoretical paper. There are no experimental setup details, hyperparameters, or system-level training settings described. |