Invoke Artificial Intelligence and Machine Learning for Strategic-Level Games and Interactive Simulations

Abstract

Computer games are an important sector of the digital economy, computer and entertainment industry are very sophisticated in many ways in the current context of technology. They’ve gone beyond entertainment needs, and the computer game paradigm and technology together are now increasingly used in education, training, storytelling, and wherever it’s necessary to create an appealing and engaging environment. More realism in virtual and artificial environments and more real interfaces to the users can be considered as two main advantages that we get using these techniques. Instead of pre-defined hard coded scripts driving these environments, we will be able to create just the environment and its relative mechanics, so the artificial intelligence could introduce tailored challenges and scenarios to the environment. This paper proposes a Behavioral Driven Procedural Content Generation methodology together with Ternary Neural Networks to be used in interactive strategy-based simulations for effective decision making. This is vital because current approaches like Experience Driven Procedural Content Generation algorithms can be very flexible and one small change could trigger complex changes in the system. Using another model created by using player behavior will be specifying the clamping conditions so the AI is capable of stabilizing itself.