Interactive Storytelling and Real-time Heuristic Search

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1. Given an opportunity to interact with their audience, human storytellers are adaptive. Bedtime stories and individualized tutoring are examples of adapting story content to the audience in order to maximize its effectiveness. Such boutique delivery is not scalable for traditional mass markets and, as a result, commercial movies and large-scale lectures follow the one-size-fits-all principle. A computer-assisted medium such as video games can combine the best of both worlds, delivering entertainment customized to each member of the audience on a mass scale. Furthermore, the audience can become an active participant in the story, interacting with the narrative through its own actions. Recent research has attacked the problem of interactive storytelling from two angles. First, narratives have been represented in a formal language so that automated planners can accommodate the audience's actions while respecting the author's narrative goals. Second, automatically acquired models of the audience's preferences are used to tailor the accommodations to them. My research group was one of the first to use automated audience modeling in interactive digital storytelling. Under the proposed research program we will first extend our existing systems with an explicit model of the audience's emotional response. This will allow our automated storyteller to customize its narrative expressly to elicit a series of specific emotional responses from the audience (e.g., hope followed by joy). Second, we will use the ability of our experience managers to automatically generate a multitude of story plots for narrative space exploration. We will start by building an automated assistant to story designers to estimate audience's response to the interactive story space beforehand. We will then add a mechanism to automatically modify the story space to ensure desired audience response. The benefits of this research include more immersive personalized entertainment and training systems, more believable virtual characters and lower development costs.**2. Artificial Intelligence agents frequently need to make decisions with incomplete information in limited time. The applications range from route planning to personal digital assistants to non-playable characters in video games. Real-time heuristic search is an approach to real-time decision-making under uncertainty and is particularly amenable to parallelism due to its strong data locality. To date, my research group has produced a progression of state-of-the-art real-time heuristic search algorithms. We achieved such performance by introducing several key paradigms to the field of real-time heuristic search. As computing hardware has become ubiquitously parallel, my research group will extend our database-driven real-time heuristic search to parallel hardware. We will analyze the resulting algorithms empirically as well as continue our work on the theory of real-time heuristic search. The benefits of this research include more intelligent autonomous agents and, in particular, more believable non-playable characters in interactive entertainment.