OPTIMIZING AN AIR DEFENSE EVALUATION MODEL USING INDUCTIVE LEARNING
Yuh-Jeng Lee, Chang-Yun Lo · Applied Artificial Intelligence · 1994
We present an effective method that integrates inductive learning in simulation modeling to analyze ship air defense combat scenarios. By using induction in a simulation environment, we are able to discover rules in a ship air defense evaluation model about the optimal weapon assignments that we might not be aware of or could not express clearly. This approach can also perform sensitivity analysis in identifying variables that are critical for certain weapon operations. In addition, results obtained from inductive learning, as represented in the format of decision trees, are easy for a user to understand, maintain, and adopt for other use.