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.
CITATION STYLE
Bindewald, J. M., Peterson, G. L., & Miller, M. E. (2015). Trajectory generation with player modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9091, pp. 42–49). Springer Verlag. https://doi.org/10.1007/978-3-319-18356-5_4
Mendeley helps you to discover research relevant for your work.