Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent. To that end, we propose the Price Negotiator, a modular deep neural network that addresses the unsolved problems in recent studies by (1) considering images of the items as a crucial, though neglected, source of information in a negotiation, (2) heuristically finding the most similar items from an external online source to predict the potential value and an acceptable agreement price, (3) predicting a general price-based action at each turn which is fed into the language generator to output the supporting natural language, and (4) adjusting the prices based on the predicted actions. Empirically, we show that our model, that is trained in both supervised and reinforcement learning setting, significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and dialogue quality.
翻译:作为在线购物的一个基本和复杂的方面,谈判对于智能剂来说仍然具有挑战性。 为此,我们建议价格谈判者,这是一个模块式的深层神经网络,解决最近研究中尚未解决的问题,其方法是:(1) 将项目图像视为谈判中至关重要但被忽视的信息来源,(2) 从外部在线来源找到最相似的项目,以预测潜在价值和可接受的协议价格,(3) 预测每个转弯都采取以价格为基础的一般行动,将其输入语言生成器,以输出支持的自然语言,(4) 根据预测的行动调整价格。 我们生动地表明,在监管和强化学习环境方面受过培训的我们的模式,在协议价格、价格一致性和对话质量方面大大改进了关于克雷格斯利斯特-巴莱昂数据集的谈判。