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..
Local Minima, Heavy Tails, and Search Effort for GBFS
Authors: Eldan Cohen, J. Christopher Beck
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present an empirical analysis of the local minima phenomenon in GBFS. We present results for the FF heuristic [Hoffmann and Nebel, 2001] with deferred ( lazy ) heuristic evaluation [Richter and Helmert, 2009], however experiments with standard ( eager ) evaluation and other heuristics (causal graphs [Helmert, 2004], landmark count [Richter et al., 2008], landmark cut [Helmert and Domshlak, 2009]) yielded similar trends. We use Fast Downward [Helmert, 2006], configured not to re-open nodes. |
| Researcher Affiliation | Academia | Eldan Cohen and J. Christopher Beck Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1 presents pseudocode for a randomized restarting GBFS (RRGBFS). |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We use the six benchmark domains in Cohen and Beck [2018], for which the constrainedness of problems can be controlled either by resource-constrainedness parameter (denoted C) or by goal-constrainedness parameter (denoted λ): No Mystery, Rovers, TPP, Maintenance, Parking, and Freecell. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions using ensembles of random problems and multiple runs on single instances, but not specific partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using "Fast Downward [Helmert, 2006]" and "FF heuristic [Hoffmann and Nebel, 2001]" but does not specify exact version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We use randomized heuristic search with a geometric restart policy [Walsh, 1999] with an initial value of 16, increasing with a factor of 1.5: (16, 24, 36, ...). |