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A Dynamic Strategy Coach for Effective Negotiation

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arxiv 1909.13426 v1 pith:X5OOPAMM submitted 2019-09-30 cs.CL

classification cs.CL
keywords negotiationtacticscoachpricesellerstrategybargainingbest
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Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine's advantage at data-driven decision-making and human's language generation ability. We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialog. Our negotiation coach monitors messages between them and recommends tactics in real time to the seller to get a better deal (e.g., "reject the proposal and propose a price", "talk about your personal experience with the product"). The best strategy and tactics largely depend on the context (e.g., the current price, the buyer's attitude). Therefore, we first identify a set of negotiation tactics, then learn to predict the best strategy and tactics in a given dialog context from a set of human-human bargaining dialogs. Evaluation on human-human dialogs shows that our coach increases the profits of the seller by almost 60%.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues

    cs.CL 2024-12 conditional novelty 6.0 of 10

    LDPP automatically discovers latent dialogue policies from raw records and uses offline hierarchical reinforcement learning to plan in that latent space, outperforming strong baselines on proactive dialogue benchmarks.

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