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第35行：
第35行： − 情感计算是研究和开发能够识别、解释、处理和模拟人类情感的系统和设备。这是一个跨越计算机科学、心理学和认知科学的跨学科领域。而这一领域的一些核心思想可以追溯到早期的情感哲学研究，陶和谭引用了这些思想。更现代的计算机科学分支起源于 Rosalind Picard 1995年的论文“ Affective C omputing”MIT 技术报告 # 321(摘要) ，1995年关于 Affective Computing 和她由 MIT 出版社出版的书 Affective Computing。这项研究的动机之一是赋予机器情商的能力，包括模拟移情。机器应该解读人类的情绪状态，并使其行为适应人类的情绪，对这些情绪作出适当的反应。+ 第67行：
第67行： − 识别情感信息需要从收集到的数据中提取出有意义的模式。这是使用机器学习技术，处理不同的模式，如语音识别，自然语言处理，或面部表情检测。大多数这些技术的目标是产生与人类感知者在相同情况下给出的标签相匹配的标签: 例如，如果一个人做出皱眉的面部表情，那么计算机视觉系统可能会被教导将他们的脸标记为看起来“困惑”、“专注”或“轻微消极”(与积极相反，它可能会说，如果他们正在以一种快乐的方式微笑)。这些标签可能与人们的真实感受相符，也可能不相符。+ 第74行：
第74行： − 情感计算的另一个领域是计算机设备的设计，提出要么展示先天的情感能力或能够令人信服地模拟情绪。基于当前的技术能力，一个更加实用的方法是模拟会话代理中的情绪，以丰富和促进人与机器之间的互动。+
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One of the motivations for the research is the ability to give machines emotional intelligence, including to simulate empathy. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions.
One of the motivations for the research is the ability to give machines emotional intelligence, including to simulate empathy. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions.
情感计算是对能够识别、解释、处理和模拟人类影响的系统和设备的研究和开发。这是一个融合计算机科学、心理学和认知科学【1】的跨学科领域。虽然该领域的一些核心思想可以追溯到早期对情感【2】的哲学研究，但计算机科学的更现代分支起源于罗莎琳德·皮卡德1995年关于情感计算的论文【3】和她的由麻省理工出版社【5】【6】出版的《情感计算》【4】。这项研究的动机之一是赋予机器情商的能力，包括模拟移情。机器应该解读人类的情绪状态，并使其行为适应人类的情绪，对这些情绪作出适当的反应。
== Areas ==
== Areas ==
Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using machine learning techniques that process different modalities, such as speech recognition, natural language processing, or facial expression detection. The goal of most of these techniques is to produce labels that would match the labels a human perceiver would give in the same situation: For example, if a person makes a facial expression furrowing their brow, then the computer vision system might be taught to label their face as appearing "confused" or as "concentrating" or "slightly negative" (as opposed to positive, which it might say if they were smiling in a happy-appearing way). These labels may or may not correspond to what the person is actually feeling.
Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using machine learning techniques that process different modalities, such as speech recognition, natural language processing, or facial expression detection. The goal of most of these techniques is to produce labels that would match the labels a human perceiver would give in the same situation: For example, if a person makes a facial expression furrowing their brow, then the computer vision system might be taught to label their face as appearing "confused" or as "concentrating" or "slightly negative" (as opposed to positive, which it might say if they were smiling in a happy-appearing way). These labels may or may not correspond to what the person is actually feeling.
识别情感信息需要从收集到的数据中提取出有意义的模式。这是通过处理不同模式的机器学习技术完成的，如语音识别、自然语言处理或面部表情检测。大多数这些技术的目标是产生与人类感知者在相同情况下给出的标签相匹配的标签: 例如，如果一个人做出皱眉的面部表情，那么计算机视觉系统可能会被教导将他们的脸标记为看起来“困惑”、“专注”或“轻微消极”(与积极相反，它可能会说，如果他们正在以一种快乐的方式微笑)。这些标签可能与人们的真实感受相符，也可能不相符。
===Emotion in machines===
===Emotion in machines===
Another area within affective computing is the design of computational devices proposed to exhibit either innate emotional capabilities or that are capable of convincingly simulating emotions. A more practical approach, based on current technological capabilities, is the simulation of emotions in conversational agents in order to enrich and facilitate interactivity between human and machine.
Another area within affective computing is the design of computational devices proposed to exhibit either innate emotional capabilities or that are capable of convincingly simulating emotions. A more practical approach, based on current technological capabilities, is the simulation of emotions in conversational agents in order to enrich and facilitate interactivity between human and machine.
情感计算的另一个领域是计算设备的设计，旨在展示先天的情感能力或能够令人信服地模拟情感。基于当前的技术能力，一个更加实用的方法是模拟会话代理中的情绪，以丰富和促进人与机器之间的互动。
[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html|title=Mind Over Matter|last=Restak|first=Richard|date=2006-12-17|work=The Washington Post|access-date=2008-05-13}}</ref>
[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref>{{cite news|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html|title=Mind Over Matter|last=Restak|first=Richard|date=2006-12-17|work=The Washington Post|access-date=2008-05-13}}</ref>