Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Multi-Agent Pathfinding Algorithms
Authors: Guni Sharon
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 8,974 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,884 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | CBS has a major drawback when solving MAPF instances that is dense with agents that conflict frequently. |
| Researcher Affiliation | Academia | Thesis Summary: Optimal Multi-Agent Pathfinding Algorithms Guni Sharon |
| Pseudocode | No | The paper describes algorithms verbally and lists components, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper discusses the problem and algorithms but does not provide concrete access information (link, DOI, citation) for any publicly available or open dataset used for training. |
| Dataset Splits | No | The paper does not specify exact dataset split percentages, sample counts, or refer to predefined splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |