  Opponent modeling consists in modeling the strategy or preferences of an
agent thanks to the data it provides. In the context of automated negotiation
and with machine learning, it can result in an advantage so overwhelming that
it may restrain some casual agents to be part of the bargaining process. We
qualify as "curious" an agent driven by the desire of negotiating in order to
collect information and improve its opponent model. However, neither
curiosity-based rational-ity nor curiosity-robust protocol have been studied in
automatic negotiation. In this paper, we rely on mechanism design to propose
three extensions of the standard bargaining protocol that limit information
leak. Those extensions are supported by an enhanced rationality model, that
considers the exchanged information. Also, they are theoretically analyzed and
experimentally evaluated.

Artificial Intelligence;Multiagent Systems