Performance assessment in training simulators for learning complex tasks is a complex task in itself. It requires monitoring and interpreting the student's behavior in the simulator using knowledge of the training task, the environment, and a lot of experience. Assessment in simulators is therefore generally done by human observers. To capture this process in an automated system is challenging and requires innovative solutions. This paper proposes a new module in TNO's Virtual Instruction platform for automated assessment in simulators that is based on Neural-Symbolic Learning and Reasoning. The module is capable of using existing rules and learning new rules for performance assessment. This is done by observing experts and students performing the training tasks. These rules can be used to automatically assess student performance in a training simulator, to validate existing rules and to support the assessment process. The rules can also be used for adaptive training by applying them backwards (relating student competences to adaptations in simulation and instructions). For training organizations, this provides a quicker, cost-saving and more objective evaluation and efficient training of the student in simulation based training, taking into account both explicit and implicit rules. The module will be developed in a three year research project on assessment in driving simulators for testing and examination and tested in various other domains, like jetfighter pilot training and strategic command and control training.