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


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.

  Title                    = {Predictability in Human-Agent Cooperation: Adapting to Humans' Personalities},
  Author                   = {Sebastian Ahrndt AND Benjamin Breitung AND Johannes F"ahndrich AND Sahin Albayrak},
  Booktitle                = {SAC '15 Proceedings of the 30th Annual ACM Symposium on Applied Computing 2015},
  Publisher                = {ACM Press},
  Year                     = {2015},

  Address                  = {Salamanca, Spain},
  Month                    = {April 13 -- 17},
  Pages                    = {474--479},
  Volume                   = {1: Artificial Intelligence & Agents, Distributed Systems, and Information Systems},

  Doi                      = {10.1145/2695664.2695702},
  Organization             = {ACM SIGAPP},
  Owner                    = {ahrndt},
  Timestamp                = {2014.10.12},
  Url                      = {}
Sebastian Ahrndt, Benjamin Breitung, Johannes Fähndrich, Sahin Albayrak
Cooperative Systems (COSYS) Track of the 30th ACM/SIGAPP Symposium on Applied Computing (SAC 2015)