Using a Basic MDP-Based Task Allocator in a Multi-Agent System with Human Participation

As multi-agent systems transition from academia to industry, the integration between non-human agents and human operators is becoming the standard. Consequently, task allocation presents a complex challenge, where both identical and non-identical agents vie for assignments. When conventional task allocation algorithms are presented with human workers, they often fail to respond, resulting in unassigned tasks. The proposed algorithm facilitates human interaction within a multi-agent, heterogeneous robotic system, enabling them to compete for available tasks and report task completion using a Markov Decision Process (MDP) based approach. Through simulations, the algorithm demonstrates significantly improved performance when operating alongside non-human systems compared to classical algorithms. This advancement empowers humans to engage in both task generation and task completion. The algorithm showcases the interaction between human and non-human agents within collaborative environments, mirroring the dynamics anticipated in real-world industrial operations.