Experimental study on fighters behaviors mining

Effective prediction for fighters behaviors is crucial for air-combats as well as for many other game fields. In this paper, we present three patterns to predict the behaviors of fighters that are the ActionStreams pattern, the Owner-Actions pattern and the Time-Owner-Actions pattern, where: (1) ActionStreams pattern is a coarse granular for describing the fighter's behaviors with action identifier whereas without distinguishing the time and the executor/owner; (2) Owner-Actions pattern is a finer granular for describing the fighter's behaviors with the action identifier and the executor whereas without distinguishing the time; and (3) Time-Owner-Actions pattern encapsulates the action identifier, the time, and also the executor. Based on such fighters' behaviors patterns, we explore the data structures used to store and the satisfied properties used to mine; and further, by designing and implementing the relevant mining/processing algorithms and systems, we have discovered some experience patterns of the fighters' behaviors and have conducted certain valid predictions for the fighters' behaviors. We also present the experimental results conducted on the simulation platform of the air to air combats. The results show that our method is effective.