A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies
<|reference_start|>A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies: Despite recent advancements in AI and NLP, negotiation remains a difficult domain for AI agents. Traditional game theoretic approaches that have worked well for two-player zero-sum games struggle in the context of negotiation due to their inability to learn human-compatible strategies. On the other hand, approaches that only use human data tend to be domain-specific and lack the theoretical guarantees provided by strategies grounded in game theory. Motivated by the notion of fairness as a criterion for optimality in general sum games, we propose a negotiation framework called FDHC which incorporates fairness into both the reward design and search to learn human-compatible negotiation strategies. Our method includes a novel, RL+search technique called LGM-Zero which leverages a pre-trained language model to retrieve human-compatible offers from large action spaces. Our results show that our method is able to achieve more egalitarian negotiation outcomes and improve negotiation quality.<|reference_end|>
@article{shea2024a,
    title={A Fairness-Driven Method for Learning Human-Compatible Negotiation
  Strategies},
    author={Ryan Shea and Zhou Yu},
    journal={arXiv preprint arXiv:2409.18335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18335},
    primaryClass={cs.AI cs.CL}
}