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].
Monte Carlo Tree Search for Multi-Robot Task Allocation
Authors: Bilal Kartal, Ernesto Nunes, Julio Godoy, Maria Gini
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach using the Solomon data set (Solomon 1987) for the vehicle routing problem with time-windows. We present preliminary results (see Table 1) on a Solomon data set with 25 tasks and three robots. |
| Researcher Affiliation | Academia | Bilal Kartal, Ernesto Nunes, Julio Godoy, and Maria Gini Department of Computer Science and Engineering University of Minnesota (bilal,enunes,godoy,gini)@cs.umn.edu |
| Pseudocode | No | The paper describes the algorithm steps and equations within the text, but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any information about open-sourcing the code for the methodology described. |
| Open Datasets | Yes | We evaluate our approach using the Solomon data set (Solomon 1987) for the vehicle routing problem with time-windows. |
| Dataset Splits | No | The paper mentions using the Solomon data set but does not provide specific details on how it was split into training, validation, or test sets, nor does it specify proportions or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | For these experiments the UCB is not tuned and each experiment is run once. For these experiments the UCB is not tuned and each experiment is run once. Our approach quickly ๏ฌnds solutions completing all the tasks, improves the distance quality, and although it takes more time on less constrained scenarios (e.g larger timewindows), it is orders of magnitude faster than exact methods. The optimal solution found by our approach is shown in Figure 1, where each robot is allocated to a task cluster. We present preliminary results (see Table 1) on a Solomon data set with 25 tasks and three robots. For these experiments the UCB is not tuned and each experiment is run once. Our approach quickly ๏ฌnds solutions completing all the tasks, improves the distance quality, and although it takes more time on less constrained scenarios (e.g larger timewindows), it is orders of magnitude faster than exact methods. The optimal solution found by our approach is shown in Figure 1, where each robot is allocated to a task cluster. |