论文标题
情感流形：建模机器的思想，喜欢，厌恶，享受，痛苦，担心，恐惧和感觉像人类
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
论文作者
论文摘要
在开发了不同的机器学习和流动学习算法之后，现在可能是将它们放在一起为机器做出强大头脑的好时机。在这项工作中，我们将情感歧管作为机器思想的组成部分。每个情感歧管都会模拟一个特征性的思想群体，并包含多个状态。我们将机器的思想定义为一组情感流形。我们使用学习模型将输入信号映射到情感歧管的嵌入空间。使用此映射，机器或机器人采用输入信号，并且可以对其进行情感反应。我们使用暹罗网络深度度量学习，并为情感流形学习提出损失功能。我们根据心理和哲学研究来定义国家之间的边缘。使用实例的三胞胎，我们训练网络以最大程度地减少每个州的差异，并在状态之间具有所需的距离。我们表明，情感歧管可以在机器机器和人机相互作用中具有各种应用。还提供了一些模拟，以验证所提出的方法。可以在机器脑海中拥有尽可能多的情感歧管。机器思想中更多的情感歧管可以使其更现实和有效。本文打开门。我们邀请来自各个科学领域的研究人员提出更多的情感流形，以插入机器的脑海。
After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as components of a machine's mind. Every affective manifold models a characteristic group of mind and contains multiple states. We define the machine's mind as a set of affective manifolds. We use a learning model for mapping the input signals to the embedding space of affective manifold. Using this mapping, a machine or a robot takes an input signal and can react emotionally to it. We use deep metric learning, with Siamese network, and propose a loss function for affective manifold learning. We define margins between states based on the psychological and philosophical studies. Using triplets of instances, we train the network to minimize the variance of every state and have the desired distances between states. We show that affective manifolds can have various applications for machine-machine and human-machine interactions. Some simulations are also provided for verification of the proposed method. It is possible to have as many affective manifolds as required in machine's mind. More affective manifolds in the machine's mind can make it more realistic and effective. This paper opens the door; we invite the researchers from various fields of science to propose more affective manifolds to be inserted in machine's mind.
