NSF Award
1647427
The goal of this project is to improve the software that generates stories automatically for virtual environments like training simulations and educational games. Specifically, the software will be able to reason about what is actually true, what each character thinks is true, what they think others think is true, and so on, to improve the way virtual characters act and make them seem more believable and more human. Current approaches to designing these narratives often assume agents know everything about others' beliefs and goals; this often leads to inconsistent or un-believable behaviors by the agents, which damage the credibility of the software and quality of the experience for their human users. The proposal will extend the lead researcher's existing narrative planning system, using an approach that lets agents consider multiple sets of beliefs that are consistent with their own and others' actions so far, ruling out situations where agents have beliefs that are inconsistent with their actions. Compared to existing approaches, this should allow the narrative planner to generate a wider variety of narratives that are also more believable to humans, as well as to handle situations such as trickery and uncertainty where reasoning about beliefs is crucial. The research team will test the software and these assumptions through several experiments that ask people to compare narratives generated by the new software to those generated by state of the art methods. If successful, the project sets the stage to improve the quality of systems where virtual agents interact with humans such as smart phone assistants, online games, automated customer chat tools, and educational software. In particular, the work will lead to training scenarios where understanding others' beliefs is crucial, such as officer-citizen interactions. The work is also interdisciplinary, ranging from computer science to psychology, and the lead researcher is committed to training young researchers to do work that crosses these intellectual boundaries and to recruiting researchers who might not otherwise participate in computer science-related research.<br/><br/>In the work, the lead researcher proposes to develop a model of agent belief based on doxastic modal logic and possible worlds reasoning suitable for use in a planning algorithm that coordinates a virtual environment. By supporting a single modal 'believes' predicate, the planner can treat the narrative search space as a Kripke structure to reason about epistemically accessible states. This improves on previous models by allowing arbitrarily nested beliefs while simultaneously reducing the burden on the virtual environment's author to write alternative scenarios, thus increasing their flexibility and expressiveness. The research team will integrate this model of beliefs into a prototype system based on the Glaive narrative planner previously developed by the lead researcher. This prototype will take advantage of Glaive's existing heuristic-driven state-space search techniques: in addition to expanding temporally accessible states, Glaive will also expand epistemically accessible states and track when an action taken by an epistemic child can be anticipated by its epistemic parent in the Kripke structure. The initial prototype will be too slow for real-time use, but it will be suitable for conducting the proposed experiments that investigate to what extent such a model improves the believability of agent behavior in automatically generated stories. In particular, the team will study whether the planner produces narratives whose structure better meets the expectations of a human audience: that is, the model will answer questions about agent beliefs more similarly to a human audience and the resulting planner will generate stories more like those composed by human authors. Further, the prototype is expected to solve certain narrative planning problems which algorithms that lack a model of agent beliefs cannot solve. These claims will be evaluated by having the new prototype and two state-of-the-art planners generate narratives for a library of scenarios to be developed by the team that rely on agents having a theory of mind for other agents, then asking both the systems and human users a number of questions about the generated narrative and agents' beliefs to evaluate how well the planners' output conforms with humans' expectations and believability.
