Dynamic Learning Approach to Artificial Intelligence Based Generated Combat Game Tactics

Game (Artificial Intelligence) is defined as the decision making process of computer-controlled opponent in video games. To improve the entertainment value of games, Adaptive game AI can be used to allowing computer-controlled opponents to fix weaknesses automatically in the game AI, and to respond to changes in human-player tactics. Dynamic Scripting is a reinforcement learning technique succeeds in endowing computercontrolled opponents with adaptive performance. Dynamic Scripting gives illusion of Human_like behavior; it plays the game for a while, evaluates its own behavior, and builds up a tactic from behaviors that it has been successful with so far. If it starts to lose, it quickly adapts and discards current tactics, trying to learn how to deal with the new threat. In Dynamic Scripting rule_bases are manually designed using domain_specific knowledge. Manually designing these knowledge bases may be time_intensive, and risks errors in analysis and encoding. In this paper we generate the tactics for the knowledge rule of Dynamic Scripting fully automatically by using Evolutionary algorithm technique. KeywordsArtificial Intelligence, Dynamic Scripting, Reinforcement learning, First Person Shooter, Finite State Machine, Zone Impact, Self Defense, Impact Factor, Intelligence Factor, Fire Action Time, Application Programming Interface, Real Time Strategy Game, Computer Role Play Games, Artificial Neural Network.