Learning to Satisfy Needs: Predictive Processing vs. Deep Learning

Soheil Human, Golnaz Bidabadi, Markus Peschl

Publikation: KonferenzbeitragKonferenzposter

Abstract

Need satisfaction has a key role in survival and wellbeing of biological cognitive agents. As a basic cognitive capability which presents early in development and throughout the lifespan, need satisfaction can be seen as an inspiring case for development of more human-like learning and thinking machines. In this paper, we first define the problem of need satisfaction and reformulate it as a learning problem. We then argue that deep learning is not an appropriate learning approach for development of computational cognitive models of need satisfaction. Finally, we introduce an alternative conceptual learning model for need satisfaction based on predictive processing (PP).
OriginalspracheEnglisch
PublikationsstatusVeröffentlicht - 2017

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