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..
Sorting Sequential Portfolios in Automated Planning
Authors: Sergio Núñez, Daniel Borrajo, Carlos Linares López
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our greedy approach produces near-optimal solutions very quickly and that it generalizes much better than an optimal solution wrt to a specific training set which has been observed to suffer from overfitting. 3. An extensive evaluation is performed with the algorithms introduced here, a random ordering algorithm and others from the literature with data from the last three IPCs. |
| Researcher Affiliation | Academia | Sergio N u nez and Daniel Borrajo and Carlos Linares L opez Computer Science Department Universidad Carlos III de Madrid (Spain) EMAIL |
| Pseudocode | No | The paper describes the algorithms (DFBn B and SLOPE) in prose, detailing their steps, but does not present them as structured pseudocode or in a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any links to its own source code for the described methodologies, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | An extensive evaluation is performed with the algorithms introduced here, a random ordering algorithm and others from the literature with data from the last three IPCs. ... http://ipc.icaps-conference.org |
| Dataset Splits | No | The paper describes training and test sets (e.g., 'training set' from IPC 2008, 'test set' from IPC 2011) but does not mention a separate validation set. |
| Hardware Specification | Yes | We have used an Intel Xeon 2.93 GHZ quad core processor with 8 GB of RAM. |
| Software Dependencies | No | The paper mentions 'CPLEX license' and refers to general techniques like 'Mixed-integer programming technique (MIP)' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The time limit T used is 1800 seconds. ... The size of the candidate and component planners sets are defined in the ranges [8, 38] and [3, 14] respectively. |