By Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou, Shrikanth S. Narayanan (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)
The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed complaints of the Fourth overseas convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The one hundred thirty five papers during this quantity set awarded including three invited talks have been rigorously reviewed and chosen from 196 submissions. The papers are geared up in topical sections on reputation and synthesis of human impact, affect-sensitive purposes, methodological matters in affective computing, affective and social robotics, affective and behavioral interfaces, correct insights from psychology, affective databases, overview and annotation tools.
Read Online or Download Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II PDF
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Additional info for Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II
Example text
Several times, a questioner would get a very close word and oscillate between related synonyms, for example “mad” and “angry”. In the future, we will deal with this by changing the stopping criteria from guessing the exact word to guessing a synonym that cannot be distinguished Emotion Twenty Questions: Toward a Crowd-Sourced Theory of Emotions 9 by any type of explanation. However, even though it may seem that two words are closely related, there may still be some differences. For example, one player was unsure about whether “proud” and “pride” would be considered equal (native speakers of English generally feel that “pride” has a negative connotation, whereas “proud” has a positive connotation).
There were some experimental effects with hinting behavior. In after-action reviews with the players, there was anecdotal evidence of retribution type experimental effects, where one player who gives only yes/no answers without any hints will be given less hints when it is their turn to be questioner. Furthermore, there is also mercy type experimental effects, whereby more hints are given by the answerer as the questioner approaches the 20 question limit. Since our main focus was the yes/no questions we did not attempt to quantify the hinting behavior as this did not effect the collection of yes/no question/answers for emotion words.
The questioner agent needs to select questions and process yes/no type answers, but the answerer agent would need to understand an open set of questions that can be asked of any emotion, so we consider the questioner role to be easier to automate than the answerer role. Our initial work on designing a questioner agent is described in [17,18] and [18] includes an interactive demo. For automating the answerer role, the computer would need a way to understand a wide variety of questions. We have undertaken previous research on how to answer questions about dimensional representations for emotions and similarity and subsethood judgments [12,13,19], which, according to our pilot data, make up nearly 25% of the total questions.
