Trajectory Generation with Player Modeling

Document Type

Conference Proceeding

Publication Date



The ability to perform tasks similarly to how a specific human would perform them is valuable in future automation efforts across several areas. This paper presents a k-nearest neighbor trajectory generation methodology that creates trajectories similar to those of a given user in the Space Navigator environment using cluster-based player modeling. This method improves on past efforts by generating trajectories as whole entities rather than creating them point-by-point. Additionally, the player modeling approach improves on past human trajectory modeling efforts by achieving similarity to specific human players rather than general human-like game-play. Results demonstrate that player modeling significantly improves the ability of a trajectory generation system to imitate a given user’s actual performance.


© 2015 Springer International Publishing Switzerland

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Funding notes: This work was supported in part through the Air Force Office of Scientific Research, Computational Cognition & Robust Decision Making Program (FA9550)

Source Publication

Advances in Artificial Intelligence. Canadian AI 2015, LNCS 9091