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Affective Goal and Task Selection for Social Robots

Affective Goal and Task Selection for Social Robots

Matthias Scheutz, Paul Schermerhorn
Copyright: © 2009 |Pages: 14
ISBN13: 9781605663548|ISBN10: 1605663549|EISBN13: 9781605663555
DOI: 10.4018/978-1-60566-354-8.ch005
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MLA

Scheutz, Matthias, and Paul Schermerhorn. "Affective Goal and Task Selection for Social Robots." Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, edited by Jordi Vallverdú and David Casacuberta , IGI Global, 2009, pp. 74-87. https://doi.org/10.4018/978-1-60566-354-8.ch005

APA

Scheutz, M. & Schermerhorn, P. (2009). Affective Goal and Task Selection for Social Robots. In J. Vallverdú & D. Casacuberta (Eds.), Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence (pp. 74-87). IGI Global. https://doi.org/10.4018/978-1-60566-354-8.ch005

Chicago

Scheutz, Matthias, and Paul Schermerhorn. "Affective Goal and Task Selection for Social Robots." In Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, edited by Jordi Vallverdú and David Casacuberta , 74-87. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-354-8.ch005

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Abstract

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.

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