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首页> 外文会议>Humaine Association Conference on Affective Computing and Intelligent Interaction >Bayesian Affect Control Theory
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Bayesian Affect Control Theory

机译：贝叶斯影响控制理论

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摘要

Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.
机译：影响控制理论是两个人之间的相互作用的数学表现，其中被列为人们在某种程度上表现，以尽量减少他们的文化情绪情绪之间的偏转量和由每种情况产生的瞬态情绪情绪。影响控制理论具有最大的似然解决方案，其中可以基于过去的交互来预测最佳行为或身份。在这里，我们制定了相同的基本原理的概率和决策理论模型，并表明这是基本理论的概括。该模型比原始理论更为富有表现力，因为它可以同时将关于行为和标识的多个假设保持为概率分布。这允许模型通过学习他们的身份和预测他们的行为来产生与人的情感可信相互作用。我们用一组模拟展示了这种概括。然后，我们展示了我们的模型如何用作与人类交互的系统的情绪化“插件”。我们通过建立一个简单的智能辅导应用程序并在20名参与者的实验中展示人类互动能力。

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