Data-Driven Alibi Story Telling for Social Believability

As computer games adopt larger, more life-like virtual worlds, socially believable characters become progressively more important. Socially believable non-player characters (NPCs) must be able to act in social situations and communicate with human players. In this paper, we address one aspect of social believability: the construction and telling of alibi stories, or an artificial background that explains what a character has been doing while not in the presence of the human player. We describe a technique for generating alibi stories and communicating the alibi stories via natural language. Our approach uses machine learning to overcome knowledge engineering bottlenecks necessary to instill intelligent characters with social behavioral knowledge. Alibi stories are subsequently generated from learned social behavioral knowledge. By leveraging the Google N-Gram Corpus and Project Gutenberg books, natural language is generated with a discourse planner and text generation that incorporate different expressivity and sentiment, which can be employed to create NPCs with a variety of personal traits.