Methods for task allocation via agent coalition formation
Artificial Intelligence
Coalition structure generation with worst case guarantees
Artificial Intelligence
Customer Coalitions in the Electronic Marketplace
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Generating Coalition Structures with Finite Bound from the Optimal Guarantees
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
An algorithm for distributing coalitional value calculations among cooperating agents
Artificial Intelligence
Reaching Agreements for Coalition Formation through Derivation of Agents' Intentions
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Optimal Coalition Structure Generation In Partition Function Games
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Near-optimal anytime coalition structure generation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An algorithm for computing optimal coalition structures in non-linear logistics domains
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Computing optimal coalition structures in non-linear logistics domains
International Journal of Intelligent Information and Database Systems
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The process of forming coalitions of software agents generally requires calculating a value for every possible coalition which indicates how beneficial that coalition would be if it was formed. Now, since the number of possible coalitions increases exponentially with the number of agents involved, having one agent calculate all the values is inefficient. Given this, we present a novel algorithm for distributing this calculation among agents in cooperative environments. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents' shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, and has minimal memory requirements. To evaluate the effectiveness of our algorithm we compare it with the only other algorithm available in the literature (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took 0.00037% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 477826101 to 0, and the total number of bytes sent between the agents dropped from 674047872 to 0 (note that for larger numbers of agents, these improvements become exponentially better).