A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies
Abstract: Despite recent advancements in AI and NLP, negotiation remains a difficult domain for AI agents. Traditional game-theoretic approaches that have worked well in 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 criteria 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.
Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: Dialogue and Interactive Systems, Efficient/Low-Resource Methods for NLP
Contribution Types: NLP engineering experiment, Approaches to low-resource settings
Languages Studied: English
Submission Number: 1941
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