  In the recent years, various 3D mixed reality serious games have been
developed for different applications such as physical training, rehabilitation,
and education. Task performance in a serious game is a measurement of how
efficiently and accurately users accomplish the game's objectives. Prior
research includes a graph-based representation of tasks, e.g. Hierarchical Task
Network (HTN), which only models a game's tasks but does not perform
assessment. In this paper, we propose Assessment HTN (A-HTN), which both models
the task efficiently and incorporates assessment logic for game objectives.
Based on how the task performance is evaluated, A-HTN automatically performs:
(a) Task-level Assessment by comparing object manipulations and (b)
Action-level Assessment by comparing motion trajectories. The system can also
categorize the task performance assessment into single user or multi-user based
on who is being assessed. We showcase the effectiveness of the A-HTN using two
3D VR serious games: a hydrometer experiment and a multi-user chemistry
experiment. The A-HTN experiments show a high correlation between instructor
scores and the system generated scores indicating that the proposed A-HTN
generalizes automatic assessment at par with Subject Matter Experts.
