A Distributed Algorithm for Parallel Multi-task Allocation Based on Profit Sharing Learning

Abstract

Task allocation via coalition formation is a fundamental research challenge in several application domains of multi-agent systems (MAS), such as resource allocation, disaster response management, and so on. It mainly deals with how to allocate many unresolved tasks to groups of agents in a distributed manner. In this paper, we propose a distributed parallel multi-task allocation algorithm among self-organizing and self-learning agents. To tackle the situation, we disperse agents and tasks geographically in two-dimensional cells, and then introduce profit sharing learning (PSL) for a single agent to search its tasks by continual self-learning. We also present strategies for communication and negotiation among agents to allocate real workload to every tasked agent. Finally, to evaluate the effectiveness of the proposed algorithm, we compare it with Shehory and Kraus' distributed task allocation algorithm which were discussed by many researchers in recent years. Experimental results show that the proposed algorithm can quickly form a solving coalition for every task. Moreover, the proposed algorithm can specifically tell us the real workload of every tasked agent, and thus can provide a specific and significant reference for practical control tasks.