1. Balancing the Needs of Many Learners  
2. Problem Formulation  
3. Simulating Parallel Prediction Problems  
4. Introspective Prediction Learners  
5. Intrinsic Rewards for Multi-prediction Learning  
6. Optimal Behavior for Multi-prediction Learning, and Approximations  
7. Experimental Setup  
8. Experiment One: Drifter-Distractor  
9. Experiment Two: Switched Drifter-Distractor Problem  
10. Experiment Three: Jumpy Eight-Action Problem  
11. Adapting the Behavior of a Horde of Demons  
12. Conclusion  