vBattle: A new framework to simulate medium-scale battles in individual-per-individual basis

Strategy games such as Warcraft^tradeor UFO^trade franchises or RPG games like Never Winter Nights^tradeor Baldur Gate^tradeare successful blockbusters in video game industry. These games are based on battles simulated individual per individual. These type of games is a very interesting scenario to develop multilevel strategies or emergent behavior in multiagent systems. This paper presents a new computational intelligence framework, named vBattle, for the evaluation of learning strategies in video games. The framework simulates a battle game in which two or more contenders are lighting in units with a high-detail individual-per-individual resolution. This simulation considers aspects of (1) actions parameters (action time, exhaustion consumption), (2) non-deterministic action resolution, (3) hierarchical intelligence (individual vs. unit strategies), and (4) scenario interaction. The vBattle framework is designed to have both a 3D visual representation of executions (either on-line or post-mortem visualization) as well as a server-based engine to perform learning tasks. This contribution presents the design principles of vBattle framework, its objectives, the possible applications for developing computational intelligence algorithms. In addition, preliminary results using a limited version of the framework are also presented.