Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)
Tuhin Kumar Biswas, Avisek Gupta, Narayan Changder, Redha Taguelmimt, Samir Aknine, Samiran Chattopadhyay, Animesh Dutta
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.
BibTeX
@article{Biswas_Gupta_Changder_Taguelmimt_Aknine_Chattopadhyay_Dutta_2024, title={Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30421}, DOI={10.1609/aaai.v38i21.30421}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Biswas, Tuhin Kumar and Gupta, Avisek and Changder, Narayan and Taguelmimt, Redha and Aknine, Samir and Chattopadhyay, Samiran and Dutta, Animesh}, year={2024}, month={Mar.}, pages={23443-23444} }