Growing up in Egypt in the 1980s, Rana el Kaliouby was fascinated by hidden languages--the rapid-fire blinks of 1s and 0s computers use to transform electricity into commands and the infinitely more complicated nonverbal cues that teenagers use to transmit volumes of hormone-laden information to each other. Culture and social stigma discouraged girls like el Kaliouby in the Middle East from hacking either code, but she wasn't deterred. When her father brought home an Atari video game console and challenged the three el Kaliouby sisters to figure out how it worked, Rana gleefully did. When she wasn't allowed to date, el Kaliouby studied her peers the same way that she did the Atari. "I was always the first one to say'Oh, he has a crush on her' because of all of the gestures and the eye contact," she says.
Sloman was one of the first in the AI community to write about the role of emotion in computing (Sloman and Croucher 1981), and I value his insight into theories of emotional and intelligent systems. Alas, Sloman's review dwells largely on some details related to unknown features of human emotion; hence, I don't think the review captures the flavor of the book. However, he does raise interesting points, as well as potential misunderstandings, both of which I am grateful for the opportunity to comment on. Sloman writes that I "welcome emotion detectors in a wide range of contexts and relationships, for example, teacher and pupil." This might sound innocuous, but its presumption of the existence of emotion detectors is not.
Recent research has demonstrated that emotion plays a key role in human decision making. Across a wide range of disciplines, old concepts, such as the classical ``rational actor" model, have fallen out of favor in place of more nuanced models (e.g., the frameworks of behavioral economics and emotional intelligence) that acknowledge the role of emotions in analyzing human actions. We now know that context, framing, and emotional and physiological state can all drastically influence decision making in humans. Emotions serve an essential, though often overlooked, role in our lives, thoughts, and decisions. However, it is not clear how and to what extent emotions should impact the design of artificial agents, such as social robots. In this paper I argue that enabling robots, especially those intended to interact with humans, to sense and model emotions will improve their performance across a wide variety of human-interaction applications. I outline two broad research topics (affective inference and learning from affect) towards which progress can be made to enable ``affect-aware" robots and give a few examples of applications in which robots with these capabilities may outperform their non-affective counterparts. By identifying these important problems, both necessary for fully affect-aware social robots, I hope to clarify terminology, assess the current research landscape, and provide goalposts for future research.
After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement. Ekman's work has been used by the FBI and police departments to identify the seeds of violent behavior in nonverbal expressions of sentiment. He has also developed the online Atlas of Emotions at the behest of the Dalai Lama. And today his research is being used to teach computer systems how to feel.