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Bargaining can be used to resolve mixed-motive games in multi-agent systems.Although there is an abundance of negotiation strategies implemented inautomated negotiating agents, most agents are based on single fixed strategies,while it is widely acknowledged that there is no single best-performingstrategy for all negotiation settings.  In this paper, we focus on bargaining settings where opponents are repeatedlyencountered, but the bargaining problems change. We introduce a novel methodthat automatically creates and deploys a portfolio of complementary negotiationstrategies using a training set and optimise pay-off in never-before-seenbargaining settings through per-setting strategy selection. Our method relieson the following contributions. We introduce a feature representation thatcaptures characteristics for both the opponent and the bargaining problem. Wemodel the behaviour of an opponent during a negotiation based on its actions,which is indicative of its negotiation strategy, in order to be more effectivein future encounters.  Our combination of feature-based methods generalises to new negotiationsettings, as in practice, over time, it selects effective counter strategies infuture encounters. Our approach is tested in an ANAC-like tournament, and weshow that we are capable of winning such a tournament with a 5.6% increase inpay-off compared to the runner-up agent.

Automated Configuration and Usage of Strategy Portfolios for Bargaining