Research On The Application Of Artificial Intelligence Techniques Based On Rules And Machine Learning In Games

Recent years, because of perfect development of graphic hardware and rendering quality in video games, artificial intelligence has become a more and more important factor for success of game developing studios. Next generation of 3D video games not only has perfect optical effect, but also advanced AI.Due to lack of game AI papers and applications, these techniques in this paper, which can improve AI level in games indeed, achieve a measure of academic and applied values.The target of paper is that building a basic graphic rendering engine first, then based on the engine, several Rules-Based AI techniques are researched and implemented, finally, some usual problems in games are resolved by some machine learning techniques.Firstly, the graphic rendering engine has an whole pipeline, the functions of the engine as follows: Object culling, Back-face removing, Euler camera model, Lighting model, Constant shading, Flat shading, Gouraud shading, 3D clipping and Depth sorting.Secondly, some Rules-Based AIs are researched. These Rules-Based AIs include: Deterministic movement algorithm, random movement algorithm, chasing (evading) algorithm,Flocking algorithm, Pattern movement technique, FSM technique and A* algorithm.Finally, These 4 problems are resolved by using Genetic Algorithm(GA) and Artificial Neural Network(ANN):1. Pathfinding.The experiment shows that the unpredictable feature of GA can improve intelligence of Pathfinding.2. spaceship landing. Chromosome consists of movement pattern of spaceship. The result shows that the unpredictable feature of GA can improve intelligence of spaceship landing, no artificial controlling needed.3. obstacle avoidance. ANN's weights are updated by GA. Sensors are simulated by 5 line segments that radiate outward from the agent body, the agent can sense the game environment by the 5 sensors. After iterating for 768 times, average fitness and best fitness of the population have been improved quickly, the ratio of avoiding successfully has been improved from 12.5% to 85%.4. Resolved mouse track recognition. ANN's weights are updated by Back Propagation algorithm. The size of training data set is 1200, error threshold is 37.0037, the size of testing data set is 1200, according to contrastive analysis of ANN and SVM, the result is that the right recognition number of testing samples for ANN is 1125, for SVM is 1185, the wrong recognition number of testing samples for ANN is 75, for SVM is 15, the precision for ANN is 93.75%, for SVM is 98.75%.Next step,AI techniques in this paper will be integrated into an AI engine,and this AI engine is expected to actual game projects.