Informed consent has become increasingly salient for data privacy and its regulation. Entities from governments to for-profit companies have addressed concerns about data privacy with policies that enumerate the conditions for personal data storage and transfer. However, increased enumeration of and transparency in data privacy policies has not improved end-users' comprehension of how their data might be used: not only are privacy policies written in legal language that users may struggle to understand, but elements of these policies may compose in such a way that the consequences of the policy are not immediately apparent. We present a framework that uses Answer Set Programming (ASP) -- a type of logic programming -- to formalize privacy policies. Privacy policies thus become constraints on a narrative planning space, allowing end-users to forward-simulate possible consequences of the policy in terms of actors having roles and taking actions in a domain. We demonstrate through the example of the Health Insurance Portability and Accountability Act (HIPAA) how to use the system in various ways, including asking questions about possibilities and identifying which clauses of the law are broken by a given sequence of events.

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.

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.

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” often limited to what can be handencoded by system authors. Proceduralist readings, a way of deriving meaning for games based on their underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher-level meanings (e.g., deriving dynamics from mechanics), opening the door to broader and more culturally-situated game generation.

We present a rule specification language called Ceptre,intended to enable rapid prototyping for experimental game mechanics, especially in domains that depend on procedural generation and multi-agent simulation. Ceptre can be viewed as an explication of a new methodology for understanding games based on linear logic, a formal logic concerned with resource usage. We present a correspondence between gameplay and proof search in linear logic, building on prior work on generating narratives. In Ceptre, we introduce the ability to add interactivity selectively into a generative model, enabling inspection of intermediate states for debugging and exploration as well as a means of play. We claim that this methodology can support game designers and researchers in designing, anaylzing, and debugging the core systems of their work in generative, multi-agent gameplay. To support this claim, we provide two case studies implemented in Ceptre, one from interactive narrative and one from a strategy-like domain.