Automated Configuration of Negotiation Strategies

TL;DR

This paper introduces an automated method to configure negotiation strategies, enabling flexible and optimized negotiation agents that outperform fixed strategies in various scenarios.

Contribution

We developed an automated algorithm configuration approach to design adaptable negotiation strategies tailored to specific settings and opponents.

Findings

Automatically configured agent outperforms all competitors by 5.1% in negotiation payoff.

Our agent's performance surpasses the top fixed-strategy agent by a significant margin.

The approach demonstrates the effectiveness of automated configuration in complex negotiation environments.

Abstract

Bidding and acceptance strategies have a substantial impact on the outcome of negotiations in scenarios with linear additive and nonlinear utility functions. Over the years, it has become clear that there is no single best strategy for all negotiation settings, yet many fixed strategies are still being developed. We envision a shift in the strategy design question from: What is a good strategy?, towards: What could be a good strategy? For this purpose, we developed a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings. By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation scenarios. To critically assess our approach, the agent was tested in an ANAC-like bilateral automated negotiation tournament setting against past competitors. We show that our automatically configured agent outperforms all other agents, with a 5.1% increase in negotiation payoff compared to the next-best agent. We note that without our agent in the tournament, the top-ranked agent wins by a margin of only 0.01%.