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 [1].
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. |