We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other techniques (such as linear regression models or boosted decision trees). In a follow-up study, we show that the most successful models change as the dataset size increases and the prediction targets change, and show that boosted decision trees may not be suitable for the negotiation domain. We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation. Further, we show that this approach can be a stepping stone from purely exploratory research to targeted human-behavioral experimentation. Through our approach, areas of social artificial intelligence that have historically benefited from expert knowledge and traditional AI approaches can be combined with more recent proven-effective machine learning algorithms.

我们提出了一种机器学习方法的结果，以分析几种人与人之间的谈判研究。通过将在一系列实证研究中收集的关于谈判行为的专家知识与神经网络相结合，我们表明，参数选择的混合方法可为设计更有效和更具社会智能的代理带来希望。具体来说，我们表明，使用理论驱动的三参数模型进行深层前馈神经网络可以有效地预测谈判结果。此外，它优于使用更多参数的其他专家设计模型以及使用其他技术的模型（例如线性回归模型或增强型决策树）。在后续研究中，我们表明，最成功的模型会随着数据集大小的增加和预测目标的变化而变化，并表明增强的决策树可能不适合协商领域。我们预计这些结果将对那些试图将广泛的领域知识与人机谈判中更自动化的方法结合起来的人产生影响。此外，我们表明，这种方法可以成为从纯粹的探索性研究到有针对性的人类行为实验的垫脚石。通过我们的方法，过去可以从专家知识和传统AI方法中受益的社会人工智能领域可以与更近期有效的机器学习算法相结合。我们表明，这种方法可以成为从纯粹的探索性研究到有针对性的人类行为实验的垫脚石。通过我们的方法，过去可以从专家知识和传统AI方法中受益的社会人工智能领域可以与更近期有效的机器学习算法相结合。我们表明，这种方法可以成为从纯粹的探索性研究到有针对性的人类行为实验的垫脚石。通过我们的方法，过去可以从专家知识和传统AI方法中受益的社会人工智能领域可以与更近期有效的机器学习算法相结合。