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 Simultaneous Maze Solving Problem
Authors: Stefan Funke, Andre Nusser, Sabine Storandt
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 shows what can be achieved with our current implementation. Experimental Results We implemented all discussed approaches in Python. Experiments were conducted on an Intel Core i7-4510U CPU and 12GB of RAM. |
| Researcher Affiliation | Academia | Stefan Funke and Andr e Nusser Universit at Stuttgart Institut f ur Formale Methoden der Informatik 70569 Stuttgart, Germany EMAIL Sabine Storandt Julius-Maximilians-Universit at W urzburg Institut f ur Informatik 97072 W urzburg, Germany EMAIL |
| Pseudocode | No | The paper describes algorithms such as Brute Force, A*, ESS, Random Sequence, Solve in Order, Iteratively Append to Sequence, and Greedy Lookahead in prose, but does not present them in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We implemented all discussed approaches in Python.' but does not provide a link or explicit statement about the code being open-source or publicly available. |
| Open Datasets | No | The paper investigates the 'Simultaneous Maze Solving Problem' for mazes of size n x m, which are generated rather than relying on a pre-existing public dataset for training or evaluation. The reference to 'The On-Line Encyclopedia of Integer Sequences' is for counting feasible mazes, not a dataset source. |
| Dataset Splits | No | The paper does not use standard machine learning training, validation, or test dataset splits; instead, it generates and solves sets of mazes. |
| Hardware Specification | Yes | Experiments were conducted on an Intel Core i7-4510U CPU and 12GB of RAM. |
| Software Dependencies | No | The paper states 'We implemented all discussed approaches in Python.' but does not provide specific version numbers for Python or any libraries/packages used. |
| Experiment Setup | Yes | In our experiments we set L to be the set of all sequences of length 3, and the value function to be the sum of the squared distances to the goal in every maze. |