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 than others. The work extends the state-of-the-art in that it does not follow a supervised learning approach requiring existing data sets.
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Notes
- 1.
Several groups have used CT to study different behavioural aspects. A list of related papers can be found here: https://goo.gl/BnsXof, last visit: 2017-14-06. We selected CT as it provides a relatively simple environment, which is complex enough to learn aspects of human behaviour as shown in existing work.
- 2.
Only one publication described an unsupervised learning technique.
- 3.
IPIP—International Personality Item Pool: A Scientific Collaboratory for the Development of Advanced Measures of Personality and Other Individual Differences—http://ipip.ori.org/. For the experiment the 100-Item Set of IPIP Big-Five Factor Markers has been used.
- 4.
Values are derived from a sample size of 5520 with a Cronbachs Alpha between .76 and .87 [13, p. 20].
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Acknowledgement
Special thanks goes to Dr.-Ing. Frank Trollmann for his insightful comments and to the anonymous reviewers for their valuable critique, suggestions, and questions.
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Ahrndt, S., Albayrak, S. (2017). Learning About Human Personalities. In: Berndt, J., Petta, P., Unland, R. (eds) Multiagent System Technologies. MATES 2017. Lecture Notes in Computer Science(), vol 10413. Springer, Cham. https://doi.org/10.1007/978-3-319-64798-2_1
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