Real-time interactive learning in the NERO video game
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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Strategy games such as Warcraft™ or UFO™ franchises or RPG games like Never Winter Nights™ or Baldur Gate™ are 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 fighting 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 postmortem 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.