InVideo Game Players Behavior Measurement using Big Five Personal Traits The propagation of video games in the previous years has led to the emergence of new areas associated with the video game industry. One of these areas is exploring the emotions and the behaviors of players after playing a specific game within a controlled environment such as a computer lab. In this paper, we will introduce a new way of analyzing the emotions and the behavior of players outside a controlled environment and using ingame data rather than using traditional questionnaires and interviews. The proposed system is expected to be part of future Internet of Things (IoT) application that is needed for human interaction. We will analyze the playeru0027s personality using ingame data. The data is generated and collected using mobile developed video game. Then, the collected data is evaluated using a wellknown personality traits model called big FiveFactor Model (FFM). In order to create a set of appropriate scenarios to analyze the playeru0027s personality based on FFM we developed a First Person Shooter (FPS) video game. Using this game, we managed to generate and collect ingame data from hundreds of people. The results show that it was able to study the playeru0027s behavior over FFM traits. It was shown that the FFM traits scores are improved by repeating the game.

Evolutionary Behavioral Design of NonPlayer Characters in a FPS Video Game Through Particle Swarm Optimization Evolutionary computation covers the family of artificial intelligence techniques inspired by nature and biological evolution. These methods, such as swarm intelligence, may have a very positive impact on video games, for instance, for the design of NonPlayer Characters (NPCs) to obtain a realistic intelligent behavior in a simple way. To this aim, we describe an evolutionary behavioral design of NPCs using particle swarm optimization in a firstperson shooter video game. Several computer experiments have been carried out to analyze the feasibility and performance of this approach. Our experimental results show that the proposed method performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.