Building and maintaining analysis-level class hierarchies using Galois Lattices
OOPSLA '93 Proceedings of the eighth annual conference on Object-oriented programming systems, languages, and applications
Customer coalitions in the electronic marketplace
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A formal method for inheritance graph hierarchy construction
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Software engineering: Systems and tools
Self-organization through bottom-up coalition formation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Forming efficient agent groups for completing complex tasks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
An algorithm for distributing coalitional value calculations among cooperating agents
Artificial Intelligence
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems
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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: 1) 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.