Predictability in Human-Agent Cooperation: Adapting to Humans' Personalities

AutorSebastian Ahrndt, Benjamin Breitung, Johannes Fähndrich, and Sahin Albayrak
QuelleCooperative Systems (COSYS) Track of the 30th ACM/SIGAPP Symposium on Applied Computing (SAC 2015) 
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Making artificial agents a constituent part of human activities leads to more affiliated teamwork scenarios and at the same time introduces several new challenges. One challenge is the team members' ability to be mutually predictable to each other, which is required to effectively plan own actions, e.g., in the field of human-aware planning. This work approaches the question whether agents are able to learn the personality of a human during interaction. In particular, we developed an agent model able to learn human personality during repeatedly played rounds in the Colored Trails Game. Human personality is described using a psychological theory of personality types known as the Five-Factor Model. The results indicate that some characteristics of a personality can be learned more accurately/easier than others.