Just as there exists varied uses for computational models of narrative, there exists a wide variety of languages aimed at representing stories. A number of them have historic roots in automated generation, for which these languages have to be limited in order to make the generation process computationally feasible. Other are focused on story understanding, with close ties to natural language making many reasoning processes computationally intractable. In this paper, we discuss the trade-off between expressivity and computational complexity of the reasoning process and argue that Impulse, a temporal, modal logic provides more expressivity than languages historically associated with story generation, while still affording reasoning capabilities. We show that these properties enable certain aspects of narrative discourse generation by using two examples from different genres, and claim that this generalizes to a broader class of problems.

Actions that affect knowledge asymmetrically between agents occur in numerous domains, from card games such as poker to the secure transmission of information. Applications in such domains often depend on reflection over knowledge, including what an agent knows about what other agents know. We are interested in enabling formal specification of these systems which may be used for executable prototyping as well as verification and other formal reasoning. Dynamic Epistemic Logic provides a formal basis for such reasoning, but is often prohibitively cumbersome to use in practice. We present an implementation and macro system called Ostari, backed by a particular flavor of Dynamic Epistemic Logic, which allows us to scale the ideas to more realistic problems. We demonstrate how actions that manipulate agents' beliefs can be written concisely and how this capability can be applied to modeling a popular card game by utilizing our system's ability to execute action sequences, answer queries about knowledge states, and find action sequences to satisfy a particular goal.

Social deduction games present a unique challenge for AI agents, because communication plays a central role in most of them, and deception plays a key role in game play. To be successful in such games, players need to come up with convincing stories, but also discern the truth of statements of other players and adapt to the information learned from them. In this paper we present an approach for virtual agents that have to determine how long to stick to their story in the light of information obtained from other players. We apply this approach to a particular social deduction game, One Night Ultimate Werewolf, and demonstrate the effect of different levels of commitment to an agent's story.