Automated Configuration and Usage of Strategy Portfolios for Bargaining

TL;DR

This paper presents an automated method for creating and deploying portfolios of negotiation strategies in bargaining scenarios, enabling agents to adapt to changing opponents and problems, leading to improved outcomes.

Contribution

It introduces a feature-based approach for automatic strategy portfolio creation and selection, enhancing adaptability in multi-agent bargaining settings.

Findings

Achieved a 5.6% higher payoff than the runner-up in a negotiation tournament.

Developed a feature representation capturing opponent and problem characteristics.

Demonstrated effective strategy selection in unseen bargaining scenarios.

Abstract

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