Embedding Information Into Game Worlds To Improve Interactive Intelligence

Current game worlds are visually rich but information poor particularly poor from the artificial intelligence (AI) point of view. Where the player sees a rich visual representation of 3D objects, internally these are just very sparsely described collections of points in space. Tools for advanced world creation, character modeling, animation, and advancements in computer graphics have brought us into the age of near photo-realistic interaction; however, these interactions are still very limited in comparison to the real world, and the information is presented overwhelmingly for the player, packaged for the Graphics Processing Unit (GPU) with little reflection or structure suitable for use by AI systems. This problem of a lack of rich information suitable for consumption by the game AT often limits the true potential for deeper levels of interaction that are becoming more in-demand by game players. This chapter presents a number of tools and techniques, which are being used to improve the embedded information contained in immersive game worlds. Symbolic annotation of the environmental elements, advanced spatial decomposition, calculating the information value of the surfaces in an interactive environment, and visual analysis form the core tools and information generators of our Common Games Understanding and Learning (CGUL) Toolkit. Using these tools to incorporate information into the game design and development process can help create information-rich interactive worlds. AI developers can work with these environmental information elements to improve non-player character (NPC) interactions both with the player and the environment, enhancing interaction, and leading to new possibilities such as meaningful in-game learning and character portability. Case studies from two different projects using these techniques provide some additional insight and reference as to how these techniques have been incorporated into current game AI and research.