Abstract
We present a novel model for studying group performance in collaborative multiagent teams. The model incorporates task interdependence and evaluation types as key factors influencing group dynamics. We propose a simulation game called CollabQuest, which will serve as a platform to explore the effects of these factors on collective performance within the context of collaborative project teams. The game involves agents collaborating to fill a common pool with a minimum amount of work within a limited number of turns, simulating a group work environment. By manipulating the composition of the group and the interdependence among agents, we plan to study how different types of tasks and evaluation approaches impact the behaviour and decision-making of agents. The model integrates intrinsic and extrinsic rewards, creating a tension between individual and collective interests, and reflecting real-world challenges. Through CollabQuest, we aim to gain insights into the challenges and strategies associated with multiagent systems in collaborative settings. This preliminary work lays the foundation for further research in the field of multiagent reinforcement learning and collective decision-making.
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Notes
- 1.
We exclude discretionary tasks from our model since the way contributions of team members are aggregated is not determined in their definition.
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López de Aberasturi-Gómez, A., Sabater-Mir, J., Sierra, C. (2023). Modelling Group Performance in Multiagent Systems: Introducing the CollabQuest Simulation Game. In: Malvone, V., Murano, A. (eds) Multi-Agent Systems. EUMAS 2023. Lecture Notes in Computer Science(), vol 14282. Springer, Cham. https://doi.org/10.1007/978-3-031-43264-4_12
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