Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
On optimal outcomes of negotiations over resources
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
An Extended Multi-Agent Negotiation Protocol
Autonomous Agents and Multi-Agent Systems
Simulation of Negotiation Policies in Distributed Multiagent Resource Allocation
Engineering Societies in the Agents World VIII
Negotiating socially optimal allocations of resources
Journal of Artificial Intelligence Research
Making markets and democracy work: a story of incentives and computing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Token Based Resource Sharing in Heterogeneous Multi-agent Teams
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
An Efficient Stochastic Clustering Auction for Heterogeneous Robotic Collaborative Teams
Journal of Intelligent and Robotic Systems
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The capability to reallocate items (e.g. tasks, securities, bandwidth slices, Mega Watt hours of electricity, and collectibles) is a key feature in automated negotiation. Especially when agents have preferences over combinations of items, this is highly nontrivial. Marginal cost based reallocation leads to an anytime algorithm where every agent's utility increases monotonically over time. Different contract types head toward different locally optimal task allocations, and contracts from a recently introduced comprehensive contract type, OCSM-contracts, head toward the global optimum. Reaching it can take impractically long, so it is important to trade off solution quality against negotiation time.To construct negotiation protocols that lead to the best achievable allocations in a bounded amount of time, we compared sequences of four contract types: original, cluster, swap, and multiagent contracts. The experiments show that it is profitable to use multiple contract types in the sequence: significantly, better solutions are reached, and faster, than if only one contract type is used. However, the best sequences only include original and cluster contracts. Swap and multiagent contracts lead to bad local optima quickly. Interestingly, the number of contracts using any given contract, type does not always decrease over time: contracts play the role of enabling further contracts.