The dominant motivational paradigm in embodied AI so far is based on the classical behaviorist approach of reward and punishment. The paper introduces a new principle based on 'flow theory'. This new, 'autotelic', principle proposes that agents can become self-motivated if their target is to balance challenges and skills. The paper presents an operational version of this principle and argues that it enables a developing robot to self-regulate its development. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Steels, L. (2004). The autotelic principle. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3139, pp. 231–242). Springer Verlag. https://doi.org/10.1007/978-3-540-27833-7_17
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