Now the vehicle no longer needs to actively move or sense in order to predict what would happen and reason about the outcomes. This vehicle builds on the previous one in order to make coherent predictions. It is not enough to just predict based on internal models, and it is important to keep the internal models updated yet separate from specific sensory experiences. In fact, he suggests that when there is a conflict, the vehicle should believe its sensors. By incorporating recent experiences, the predictor can be improved and updated, so it is kept accurate.
Prediction is useful from the simplest to the most complex tasks.
For example, even just following another vehicle can benefit from prediction, in order to eliminate confusing in the presence of obstacles, sensory occlusions, and other causes of partial observability. This vehicle introduces the use of bias in the decision making. By encoding some notion of preferences into the vehicles thresholds, we can generate behavior that appears to favor pleasant behaviors and outcomes. By manipulating thresholds we can in fact introduce any kind of bias we want and thus affect the output behavior accordingly. As we will see soon, this idea of internal bias can be related and used in reinforcement learning , where internal bias can be a source of internal reward or punishment , i.e., internal reinforcement .