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We present a novel bilateral negotiation model that allows a self-interestedagent to learn how to negotiate over multiple issues in the presence of userpreference uncertainty. The model relies upon interpretable strategy templatesrepresenting the tactics the agent should employ during the negotiation andlearns template parameters to maximize the average utility received overmultiple negotiations, thus resulting in optimal bid acceptance and generation.Our model also uses deep reinforcement learning to evaluate threshold utilityvalues, for those tactics that require them, thereby deriving optimal utilitiesfor every environment state. To handle user preference uncertainty, the modelrelies on a stochastic search to find user model that best agrees with a givenpartial preference profile. Multi-objective optimization and multi-criteriadecision-making methods are applied at negotiation time to generatePareto-optimal outcomes thereby increasing the number of successful (win-win)negotiations. Rigorous experimental evaluations show that the agent employingour model outperforms the winning agents of the 10th Automated NegotiatingAgents Competition (ANAC'19) in terms of individual as well as social-welfareutilities.

Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues