Learning about Human Personality

Abstract

This work approaches the question whether or not agents are able to learn the personality of a human during interaction. We develop two agent-models to learn about the personality of humans during repeatedly played rounds in the Colored Trails Game. Human personality is described using a psychological theory of personality traits known as the Five-Factor Model. The results show that some characteristics of a personality can be learned more accurately and easily than others. The work extends the state-of-the-art in that it does not follow a supervised learning approach requiring existing data sets.

@InCollection{Ahrndt2017,
  author       = {Sebastian Ahrndt AND Sahin Albayrak},
  title        = {Learning about Human Personality},
  booktitle    = {15th German Conference on Multiagent System Technologies (MATES 2017)},
  year         = {2017},
  booksubtitle = {15th German Conference, MATES 2017, Leipzig, Deutschland, August 23-36, 2017. Proceedings},
  series       = {Lecture Notes in Artificial Intelligence},
isbn = {978-3-319-64798-2},
doi = {https://doi.org/10.1007/978-3-319-64798-2_1},
  note         = {{To appear.}},
  publisher    = {Springer International Publishing},
  pages        = {1--18},
  owner        = {ahrndt},
  timestamp    = {2015.09.21},
}

Autoren:

Sebastian Ahrndt, Sahin Albayrak

Kategorie:

Tagungsbeitrag

Jahr:

2017

Ort:

15th German Conference on Multiagent System Technologies (MATES 2017)