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..
Envelope-Based Approaches to Real-Time Heuristic Search
Authors: Kevin Gall, Bence Cserna, Wheeler Ruml2351-2358
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that intra-envelope search is beneficial in state spaces that are highly interconnected, such as those for grid pathfinding. |
| Researcher Affiliation | Academia | Kevin C. Gall, Bence Cserna, Wheeler Ruml Department of Computer Science University of New Hampshire, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: TBA*... Algorithm 2: Intra-Envelope Search (I-ES)... Algorithm 3: Envelope Search Backward Strategy |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology is publicly available, nor does it provide a link to a repository. |
| Open Datasets | Yes | We tested variants of TBA* and I-ES on: 1) the 100 15puzzles of Korf (1985) using Manhattan distance (MD); 2) a deterministic version of the racetrack game (Barto, Bradtke, and Singh 1995)...; 3) grid pathfinding using four-way movement and MD in a) the Starcraft cauldron map (Sturtevant 2012) and b) 1500 x 1500 grids with large randomly-generated minima, referred to as the Minima domain (Figure 2 shows example maps). |
| Dataset Splits | No | The paper describes testing on 'test instances' but does not specify explicit dataset splits (e.g., train/validation/test percentages, counts, or cross-validation setup). |
| Hardware Specification | No | The paper mentions 'Each experiment was given 7GB RAM and 5 minutes' but does not specify any particular CPU, GPU, or other detailed hardware components used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation of the experiments. |
| Experiment Setup | Yes | The algorithm takes a parameter bound which is a resource limit on the iteration execution time. This limit is split into 2 parts by multiplying by a factor 0 < r1 < 1. In our experimentation we used r1 = 0.8, implying more frontier expansion and less search within the envelope. |