Multi-Robot Auctions for Allocation of Tasks with Temporal Constraints
Authors: Ernesto Nunes, Maria Gini
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the performance of the algorithm in simulation with different numbers of tasks and robots, and compare it with a baseline greedy algorithm and a state-of-the-art auction algorithm. |
| Researcher Affiliation | Academia | Ernesto Nunes and Maria Gini Department of Computer Science and Engineering University of Minnesota 200 Union St SE, Minneapolis, MN 55455 {enunes, gini}@cs.umn.edu |
| Pseudocode | Yes | Algorithm 1: Te SSI algorithm for the auctioneer |
| Open Source Code | No | The paper does not include any statement about releasing its source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Experiment 3. We used the spatial and temporal data in the Solomon s dataset (Solomon 1987), a standard dataset used for VRP with time windows. |
| Dataset Splits | No | The paper describes how the synthetic data and Solomon's dataset instances were generated or used for simulation, but it does not specify explicit training, validation, or test dataset splits for model training or evaluation in the typical machine learning context. |
| Hardware Specification | No | The paper describes running simulations for its experiments, but it does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for these simulations. |
| Software Dependencies | No | The paper mentions the 'JADE (Bellifemine, Poggi, and Rimassa 1999) multi-agent platform' was used for Experiment 2, but it does not specify a version number for JADE or any other software dependencies with their versions. |
| Experiment Setup | Yes | In our experiments we set α = 0.5. The algorithms were run on 30 instances for each number of tasks in dataset 1 and each number of robots in dataset 2 for Experiment 1, 30 for Experiment 2, and 8–12 instances for Experiment 3. |