Trajectory Generation with Player Modeling
Document Type
Conference Proceeding
Publication Date
4-2015
Abstract
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.
Source Publication
Advances in Artificial Intelligence. Canadian AI 2015, LNCS 9091
Recommended Citation
Bindewald, J.M., Peterson, G.L., Miller, M.E. (2015). Trajectory Generation with Player Modeling. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science (LNCS), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_4
Comments
© 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)