On maximal classes of utility functions for efficient one-to-one negotiation

Authors:
Yann Chevaleyre;Ulle Endriss;Nicolas Maudet
Affiliations:
LAMSADE, Université Paris-Dauphine;Department of Computing, Imperial College London;LAMSADE, Université Paris-Dauphine
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

Citing 7
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Abstract

We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of discrete resources. In this framework, reaching an optimal allocation potentially requires very complex multilateral deals. Therefore, we are interested in identifying classes of utility functions such that any negotiation conducted by means of deals involving only a single resource at at time is bound to converge to an optimal allocation whenever all agents model their preferences using these functions. We show that the class of modular utility functions is not only sufficient but also maximal in this sense.