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..
Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search
Authors: Maximilian Fickert, Tianyi Gu, Leonhard Staut, Wheeler Ruml, Joerg Hoffmann, Marek Petrik9827-9834
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly. |
| Researcher Affiliation | Academia | 1Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany 2Department of Computer Science, University of New Hampshire, USA |
| Pseudocode | Yes | Algorithm 1: Nancy; Algorithm 2: Risk-Based Lookahead; Algorithm 3: Risk Value of a Top-Level Action TLA |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We used h LM-cut (Helmert and Domshlak 2009) due to it being a popular admissible heuristic... ...We ran the experiments on a cluster of Intel Xeon E5-2660 machines using the lab framework (Seipp et al. 2017). ...We also evaluate our algorithms on the classic 100 15-puzzle instances published by Korf (1985). |
| Dataset Splits | No | The paper describes an 'offline training phase' for generating data for DDNancy's belief distributions and mentions evaluating on instances, but does not specify explicit training/validation/test splits for the models evaluated in the main experiments. |
| Hardware Specification | Yes | We ran the experiments on a cluster of Intel Xeon E5-2660 machines |
| Software Dependencies | No | The paper mentions software like 'Fast Downward' and 'lab framework' along with their respective foundational papers, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We test lookaheads of size 100, 300, and 1,000 nodes. ... used the standard limits of 30 minutes and 4 GB memory in all experiments. ... we used weighted A* with a weight of 2 and the same heuristic function that will be used online. |