Exploiting causal domain knowledge for learning to control dynamic systems
Achim G. Hoffmann · 1994
This paper introduces a simple yet effective method for using causal domain knowledge for learning to control dynamic systems. Elementary qualitative causal dependencies of the domain are exploited in order to dramatically speed up the learning of reliable control strategies from a simulation model of the system. The reliability of the obtained control strategies is strengthened as well. The effectiveness of the method has experimentally been studied at the problem of learning to balance a pole.