Exploring Stanislavskian Performance for Agent-based Nonplayer Characters through Defeasible Logic

Common approaches to behavioral artificial intelligence, like behavior trees, utility-based approaches, and machine learning are often lacking with regards to expressivity of the decision-making process. Behavior trees are too rigid; utility-based approaches are focused on outcomes, not process; and machine learned agents seek only to maximize reward and minimize loss. A combination of AI approaches, however, may provide more nuanced, visible, and coherent processes that can be realized through the performance of character behaviors. In this extended abstract, we present Viv: a system that blends defeasible logic programming with dynamic behavior trees. To our knowledge, no extant works have bridged the gap between defeasible reasoning and action, particularly in regards to the performances of virtual nonplayer characters. We begin by explaining the design of our system, which was heavily influenced by the Stanislavski method of acting. We then describe the technical implementation of our system. Finally, we describe future directions of research that could provide benefit to, or benefit from, our system.