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Choice poetics is a formalist framework that seeks to concretely describe the impacts choices have on player experiences within narrative games. Developed in part to support algorithmic generation of narrative choices, the theory includes a detailed analytical framework for understanding the impressions choice structures make by analyzing the relationships among options, outcomes, and player goals. The theory also emphasizes the need to account for players’ various modes of engagement, which vary both during play and between players. In this work, we illustrate the non-computational application of choice poetics to the analysis of two different games to further develop the theory and make it more accessible to others. We focus first on using choice poetics to examine the central repeated choice in “Undertale,” and show how it can be used to contrast two different player types that will approach a choice differently. Finally, we give an example of fine-grained analysis using a choice from the game “Papers, Please,” which breaks down options and their outcomes to illustrate exactly how the choice pushes players towards complicity via the introduction of uncertainty. Through all of these examples, we hope to show the usefulness of choice poetics as a framework for understanding narrative choices, and to demonstrate concretely how one could productively apply it to choices “in the wild.” 

Open world games present players with more freedom than games with linear progression structures. However, without clearly-defined objectives, they often leave players without a sense of purpose. Most of the time, quests and objectives are hand-authored and overlaid atop an open world's mechanics. But what if they could be generated organically from the gameplay itself? The goal of our project was to develop a model of the mechanics in Minecraft that could be used to determine the ideal placement of objectives in an open world setting. We formalized the game logic of Minecraft in terms of logical rules that can be manipulated in two ways: they may be executed to generate graphs representative of the player experience when playing an open world game with little developer direction; and they may be statically analyzed to determine dependency orderings, feedback loops, and bottlenecks. These analyses may then be used to place achievements on gameplay actions algorithmically. 

Policy design is an important part of software development. As security breaches increase in variety, designing a security policy that addresses all potential breaches becomes a nontrivial task. A complete security policy would specify rules to prevent breaches. Systematically determining which, if any, policy clause has been violated by a reported breach is a means for identifying gaps in a policy. Our research goal is to help analysts measure the gaps between security policies and reported breaches by developing a systematic process based on semantic reasoning. We propose SEMAVER, a framework for determining coverage of breaches by policies via comparison of individual policy clauses and breach descriptions. We represent a security policy as a set of norms. Norms (commitments, authorizations, and prohibitions) describe expected behaviors of users, and formalize who is accountable to whom and for what. A breach corresponds to a norm violation. We develop a semantic similarity metric for pairwise comparison between the norm that represents a policy clause and the norm that has been violated by a reported breach. We use the US Health Insurance Portability and Accountability Act (HIPAA) as a case study. Our investigation of a subset of the breaches reported by the US Department of Health and Human Services (HHS) reveals the gaps between HIPAA and reported breaches, leading to a coverage of 65%. Additionally, our classification of the 1,577 HHS breaches shows that 44% of the breaches are accidental misuses and 56% are malicious misuses. We find that HIPAA's gaps regarding accidental misuses are significantly larger than its gaps regarding malicious misuses. 

A challenge of game design is in providing affordances to players so that they can learn and improve their skills. Ad- vances in Procedural Content Generation (PCG) suggest this type of game content is a candidate for automatic creation. Some work in PCG has been successful in customizing game difficulty to achieve desired player experience; however, this often involves bringing the difficulty of the game to a level appropriate for the player’s current skills. Players desiring to improve their performance in a particular game may be will- ing to tolerate relatively higher levels of frustration and anx- iety than are targeted in experience-based approaches. As an initial step in this line of inquiry, we introduce ProcDefense, an action game with a modular difficulty control interface, as a platform for future inquiry into the effectiveness of differing PCG techniques for performance-training, dynamic difficulty adjustment.