Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
Authors: Taoan Huang, Sven Koenig, Bistra Dilkina11246-11253
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {taoanhua, skoenig, dilkina}@usc.edu |
| 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. |