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.
Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search
Authors: Jiaoyang Li, Ariel Felner, Eli Boyarski, Hang Ma, Sven Koenig
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,692 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,051 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 | Empirically, CBS with either new heuristic significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50. |
| Researcher Affiliation | Academia | 1University of Southern California 2 Ben Gurion University of the Negev |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code. |
| Open Datasets | No | We generate 50 instances with random start and goal vertices for each map and each number of agents. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | Yes | Our code is written in C++, and our experiments are conducted on a 2.80 GHz Intel Core i7-7700 laptop with 8 GB RAM. |
| Software Dependencies | No | The paper states, "Our code is written in C++", but does not provide specific version numbers for C++ or any libraries/frameworks used. |
| Experiment Setup | Yes | We generate 50 instances with random start and goal vertices for each map and each number of agents. Our code is written in C++, and our experiments are conducted on a 2.80 GHz Intel Core i7-7700 laptop with 8 GB RAM. We use a time limit of 1 minute for each solver on each instance. |