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..
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
Authors: Taoan Huang, Sven Koenig, Bistra Dilkina11246-11253
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark maps indicate that our approach, ML-guided CBS, significantly improves the success rates, search tree sizes and runtimes of the current state-of-the-art CBS solver. |
| Researcher Affiliation | Academia | Taoan Huang, Sven Koenig, Bistra Dilkina University of Southern California EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the open-source release of the code for the methodology described in this paper. |
| Open Datasets | Yes | We use a set of six four-neighbor grid maps M of different sizes and structures as the graphs underlying the instances and evaluate our algorithms on them. M includes (1) a warehouse map (Li et al. 2020); (2) the room map room-32-32-4 (Stern et al. 2019); (3) the maze map maze-128-128-2 (Stern et al. 2019); (4) the random map; (5) the city map Paris 1 256 (Stern et al. 2019); (6) the game map. |
| Dataset Splits | No | The paper states 'We obtain two sets of instances, a training dataset ITrain and a test dataset ITest' for data collection and model learning, and similar splits for experimental evaluation. However, it does not explicitly define a separate 'validation' dataset or split. |
| Hardware Specification | Yes | The experiments are conducted on 2.4 GHz Intel Core i7 CPUs with 16 GB RAM. |
| Software Dependencies | No | The paper mentions using an 'open-source software package (Joachims 2006) that implements a Support Vector Machine (SVM) approach (Joachims 2002)' and 'C++ code for CBSH2 with the WDG heuristic made available by Li et al. (2019a)', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set the regularization parameter C = 1/100 to train an SV M rank (Joachims 2002) with a linear kernel to obtain each of the ranking functions. We varied C {1/10, 1/100, 1/1000} and achieved similar results. |