Winner determination in combinatorial auction generalizations
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Combinatorial auctions with k-wise dependent valuations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The complexity of contract negotiation
Artificial Intelligence
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Multiagent resource allocation defines the issue of having to distribute a set of resources among a set of agents, aiming at a fair and efficient allocation Resource allocation procedures can be evaluated with regard to properties such as budget balance and strategy-proofness Designing a budget-balanced and strategy-proof allocation procedure that always provides a fair (namely, envy-free) and efficient (namely, Pareto-optimal) allocation poses a true challenge To the best of our knowledge, none of the existing procedures combines all four properties Moreover, in previous literature no attention is given to the allocation of unwanted resources (i.e., resources that seem to be of no use for all agents) in a way as to maximize social welfare Yet, dealing inappropriately with unwanted resources may decrease each agent's benefit Therefore, we extend the scope of sealed-bid auctions by means of involving market prices so as to always provide an optimal solution under consideration of each agent's preferences We present a new market-affected sealed-bid auction protocol (MSAP) where agents submit sealed bids on indivisible resources, and we allow monetary side-payments We show this protocol to be budget-balanced and weakly strategy-proof, and to always provide an allocation that maximizes both utilitarian and egalitarian social welfare, and is envy-free and Pareto-optimal.