Cooperative Agents Based-Decentralized and Scalable Complex Task Allocation Approach Pro Massive Multi-Agents System

A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. In previous work, we have developed a decentralized and scalable method for complex task allocation for Massive Multi-Agent System (MMAS). The method was based on two steps: I) hierarchical organization of agent groups using Formal Concepts Analysis approach (FCA) and 2) computing the optimal allocation. The second step distributes the tasks allocation process among all agent groups as follows: i. Each local allocator proposes a local allocation, then ii. The global allocator computes the global allocation by resolution of eventual conflict situations. Nevertheless, a major boundary of the method used to compute the global allocation is its centralized aspect. Moreover, conflicts process is a greedy solution. In fact, if a conflict is detected steps i) and ii) are reiterated until a non conflict situation is attained. This paper extends our last approach by distributing the global allocation process among all agents. It provides a solution based on cooperation among agents. This solution prohibits generation of conflicts. It's based on the idea that each agent picks out its own sub-task.