On the Role of Emotion in Embodied Cognitive Architectures: From Organisms to Robots

Ziemke, Tom; Lowe, Robert

doi:10.1007/s12559-009-9012-0

Cited by 92 publications

(59 citation statements)

References 52 publications

(74 reference statements)

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“…The idea is to use robots which may understand emotions, and take part in the society cooperatively, according to the emotional state received from humans. Improving the communicative behavior of robots is urgent if people are to accept and integrate them in their world representation [2]. Robots have to be spontaneous, polite and must learn how to react according to the human being emotional charge, providing a friendly environment.…”

Section: Introductionmentioning

confidence: 99%

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Rázuri¹,

Sundgren²,

Rahmani³

et al. 2015

ijarai

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Abstract-For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are efficient carriers of emotions. The features of the sound of a spoken voice probably contains crucial information on the emotional state of the speaker, within this framework, a machine might use such properties of sound to recognize emotions. This work evaluated six different kinds of classifiers to predict six basic universal emotions from non-verbal features of human speech. The classification techniques used information from six audio files extracted from the eNTERFACE05 audiovisual emotion database. The information gain from a decision tree was also used in order to choose the most significant speech features, from a set of acoustic features commonly extracted in emotion analysis. The classifiers were evaluated with the proposed features and the features selected by the decision tree. With this feature selection could be observed that each one of compared classifiers increased the global accuracy and the recall. The best performance was obtained with Support Vector Machine and bayesNet.

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Section: Introductionmentioning

confidence: 99%

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Rázuri¹,

Sundgren²,

Rahmani³

et al. 2015

ijarai

View full text Add to dashboard Cite

show abstract

“…Further arguments on the computational models of emotions can be found in the literature (Sloman, 2001;Breazeal, 2003;Arbib and Fellous, 2004;Ziemke and Lowe, 2009). As will be shown hereinafter, each of the exemplary systems described below in Subsection 4 can be (freely) attributed to a subset of the above characterizations.…”

Section: Classification Of Computational Models Of Emotionsmentioning

confidence: 99%

Computational Approaches to Modeling Artificial Emotion – An Overview of the Proposed Solutions

Kowalczuk

Czubenko

2016

Front. Robot. AI

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A cybernetic approach to modeling artificial emotion through the use of different theories of psychology is considered in this paper, presenting a review of twelve proposed solutions: ActAffAct, FLAME, EMA, ParleE, FearNot!, FAtiMA, WASABI, Cathexis, KARO, MAMID, FCM, and xEmotion. The main motivation for this study is founded on the hypothesis that emotions can play a definite utility role of scheduling variables in the construction of intelligent autonomous systems, agents, and mobile robots. In this review, we also include an innovative and panoptical, comprehensive system, referred to as the Intelligent System of Decision-making (ISD), which has been employed in practical applications of various autonomous units, and which applies xEmotion, taking into consideration the personal aspects of emotions, affects (short-term emotions), and mood (principally, long-term emotions).

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“…Likewise, Ziemke [92] and Vernon [93] provide somewhat visual approaches to modelling 'higher level' cognition. However, none of these visualization schemes aspires to provide a comprehensive pictographic language of the form described in this paper.…”

Section: Related Workmentioning

confidence: 99%

Introducing a Pictographic Language for Envisioning a Rich Variety of Enactive Systems with Different Degrees of Complexity

Moore

2016

International Journal of Advanced Robotic Systems

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Notwithstanding the considerable amount of progress that has been made in recent years, the parallel fields of cognitive science and cognitive systems lack a unifying methodology for describing, understanding, simulating and implementing advanced cognitive behaviours. Growing interest in 'enactivism' -as pioneered by the Chilean biologists Humberto Maturana and Francisco Varela -may lead to new perspectives in these areas, but a common framework for expressing many of the key concepts is still missing. This paper attempts to lay a tentative foundation in that direction by extending Maturana and Varela's pictographic depictions of autopoietic unities to create a rich visual language for envisioning a wide range of enactive systems -natural or artificial -with different degrees of complexity. It is shown how such a diagrammatic taxonomy can help in the comprehension of important relationships between a variety of complex concepts from a pan-theoretic perspective. In conclusion, it is claimed that visual language is not only valuable for teaching and learning, but also offers important insights into the design and implementation of future advanced robotic systems.

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On the Role of Emotion in Embodied Cognitive Architectures: From Organisms to Robots

Cited by 92 publications

References 52 publications

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Computational Approaches to Modeling Artificial Emotion – An Overview of the Proposed Solutions

Introducing a Pictographic Language for Envisioning a Rich Variety of Enactive Systems with Different Degrees of Complexity

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