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Autonomous Agents and Multi-Agent Systems

, Volume 29, Issue 4, pp 537–568 | Cite as

Emergence of emotional appraisal signals in reinforcement learning agents

  • Pedro SequeiraEmail author
  • Francisco S. Melo
  • Ana Paiva
Article

Abstract

The positive impact of emotions in decision-making has long been established in both natural and artificial agents. In the perspective of appraisal theories, emotions complement perceptual information, coloring our sensations and guiding our decision-making. However, when designing autonomous agents, is emotional appraisal the best complement to the perceptions? Mechanisms investigated in affective neuroscience provide support for this hypothesis in biological agents. In this paper, we look for similar support in artificial systems. We adopt the intrinsically motivated reinforcement learning framework to investigate different sources of information that can guide decision-making in learning agents, and an evolutionary approach based on genetic programming to identify a small set of such sources that have the largest impact on the performance of the agent in different tasks, as measured by an external evaluation signal. We then show that these sources of information: (i) are applicable in a wider range of environments than those where the agents evolved; (ii) exhibit interesting correspondences to emotional appraisal-like signals previously proposed in the literature, pointing towards our departing hypothesis that the appraisal process might indeed provide essential information to complement perceptual capabilities and thus guide decision-making.

Keywords

Emotions Appraisal theory Intrinsic motivation  Genetic programming Reinforcement learning 

Notes

Acknowledgments

This work was partially supported by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) under project PEst-OE/EEI/LA0021/2013 and the EU project SEMIRA through the grant ERA-Compl /0002/2009. The first author acknowledges Grant SFRH/BD/38681/2007 from the FCT.

Supplementary material

Supplementary material 1 (mp4 10873 KB)

Supplementary material 2 (mp4 10955 KB)

Supplementary material 3 (mp4 11009 KB)

Supplementary material 4 (mp4 10951 KB)

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Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.INESC-ID/Instituto Superior Técnico, Universidade de LisboaPorto SalvoPortugal

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