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..
The Fewer the Merrier: Pruning Preferred Operators with Novelty
Authors: Alexander Tuisov, Michael Katz
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation shows the practical benefit of our suggested approach, compared to the currently used methods. |
| Researcher Affiliation | Collaboration | 1Technion, Haifa, Israel 2IBM Research, Yorktown Heights, NY, USA |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. The methods are described in narrative text and formal definitions. |
| Open Source Code | Yes | The code is at https://github.com/IBM/FD-Novelty-PO |
| Open Datasets | Yes | The benchmark set consists of all STRIPS benchmarks from the satisficing tracks of International Planning Competitions (IPC) 1998-2018, a total of 1816 tasks in 64 domains. |
| Dataset Splits | No | The paper mentions using 'all STRIPS benchmarks from the satisficing tracks of International Planning Competitions (IPC) 1998-2018', but does not specify training, validation, or test dataset splits in the typical machine learning sense. |
| Hardware Specification | Yes | The experiments were performed on Intel(R) Xeon(R) Gold 6248 CPU @2.50GHz machines, with the time and memory limit of 30min and 4GB, respectively |
| Software Dependencies | No | The paper states, 'we implemented it on top of the Fast Downward planning system [Helmert, 2006].' However, it does not provide specific version numbers for Fast Downward or any other software dependencies. |
| Experiment Setup | Yes | All tested configuration perform a greedy best-first search with delayed evaluation and multiple queues. ... with the time and memory limit of 30min and 4GB, respectively. ... We select top k elements and test three bounds, k {10, 100, 1000}. |