Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)
Abstract
Simultaneous Coalition Structure Generation and Assignment (SCSGA) is an important research problem in multi-agent systems. Given n agents and m tasks, the aim of SCSGA is to form m disjoint coalitions of n agents such that between the coalitions and tasks there is a one-to-one mapping, which ensures each coalition is capable of accomplishing the assigned task. SCSGA with Multi-dimensional Features (SCSGA-MF) extends the problem by introducing a d-dimensional vector for each agent and task. We propose a heuristic algorithm called Multiple Distance Metric (MDM) approach to solve SCSGA-MF. Experimental results confirm that MDM produces near optimal solutions, while being feasible for large-scale inputs within a reasonable time frame.
Cite
Text
Biswas et al. "Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30421Markdown
[Biswas et al. "Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/biswas2024aaai-coalition/) doi:10.1609/AAAI.V38I21.30421BibTeX
@inproceedings{biswas2024aaai-coalition,
title = {{Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)}},
author = {Biswas, Tuhin Kumar and Gupta, Avisek and Changder, Narayan and Taguelmimt, Redha and Aknine, Samir and Chattopadhyay, Samiran and Dutta, Animesh},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23443-23444},
doi = {10.1609/AAAI.V38I21.30421},
url = {https://mlanthology.org/aaai/2024/biswas2024aaai-coalition/}
}