Modern software development methodologies include reuse of open source code. Reuse can be facilitated by architectural knowledge of the software, not necessarily provided in the documentation of open source software. The effort required to comprehend the system's source code and discover its architecture can be considered a major drawback in reuse. In a recent study we examined the correlations between design metrics and classes' architecture layer. In this paper, we apply our methodology in more open source projects to verify the applicability of our method.

Agency, being one's ability to perform an action and have some influence over the world, is fundamental to interactive entertainment. Although much of the games industry is concerned with providing more agency to its players, what seems to matter more is how much agency each player will actually perceive. In this paper, we present a computational model of this phenomena, based on the notion that the amount of agency that one perceives depends on how much they desire the outcomes that result from their decisions. Using a structure for high-agency stories that we designed specifically for this intent, we present the results of a 141-participant user study that tests our model's ability to select subsequent events in an original interactive story. Using a newly validated survey instrument for measuring both agency and fun, we found with a high degree of confidence that event sequences selected by our model result in players perceiving more agency than players who experience event sequences that our model does not recommend.

Gameplay in real-time strategy games seems somehow to be confined to a de facto standard where economical micro-management is equally important as combat strategy, if not more important. To enable stronger combat-oriented gameplay without sacrificing other key aspects of the genre, we propose an automated system for scheduling unit training, which we believe may allow the exploration of new paradigms of play. To be accepted by the player, such a system must, among other things, be efficient and reliable, which is a non-trivial task considering the highly dynamic nature of the environment in this genre of games. To overcome such a challenge, we propose a system inspired in the swarm intelligence demonstrated by social insects, namely wasps, and describe its limitations and benefits, based on the evaluation of an implementation of the approach as a modification of the game Warcraft III The Frozen Throne (Blizzard Entertainment, 2003).

The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. “Tech trees” or “build trees” are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players’ data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.

Conflict is an essential element of interesting stories. In previous work, we proposed a formal model of narrative conflict. We also described 7 dimensions which can be used to distinguish one conflict from another: participants, subject, duration, balance, directness, intensity, and resolution. This paper presents the results of an experiment designed to measure how well our metrics for balance, directness, intensity, and resolution predict the responses of human readers when asked to measure these same values in a set of four stories. We conclude that our metrics are able to rank stories similarly to human readers.

Analogy-based Story Generation (ASG) is a relatively under-explored approach for story generation and computational narrative. In this paper, we present the SAM (Story Analogies through Mapping) algorithm as our attempt to expand the scope and complexity of stories generated by ASG. Comparing with existing work and our prior work, there are two main contributions of SAM: it employs 1) analogical reasoning both at the specific story content and general domain knowledge levels, and 2) temporal reasoning about the story (phase) structure in order to generate more complex stories. We illustrate SAM through a few example stories.

Since early days of Artificial Intelligence (AI), one of the We present a computational approach for creating new goals has been to procedurally simulate the human ability types of magical and science fiction objects by of storytelling. Many story generation systems (Meehan extrapolating and combining existing object types. The 1981; Lebowitz 1985; Turner 1992; Pérez y Pérez and approach described here augments the creativity of planbased Sharples 2001; Cavazza, Charles, and Mead 2002; Riedl story generators such as that by Riedl and Young and Young 2010; Gervás et al. 2005) begin with a (2006). We empower a traditional story planner with the predefined world configuration. Such configurations ability to plan with analogies. We incrementally modify include unchangeable facts about the fictional world such behaviors of known objects based on a consistent set of as what objects exist, how they relate to each other and analogies with backward chaining and combine behaviors what events can happen. With the initial world of multiple objects to create a new behavior. The process configuration, story generators build stories, the execution results in a new gadget that can cause desired changes in of which transform and evolve the world. As most story the fictional world that are impossible or improbable to generators accept the initial world as a given rather than achieve by other means.

Meaningful choice has often been identified as a key component in a player's engagement with an interactive narrative, but branching stories require tremendous amounts of hand-authored content, in amounts that increase exponentially rather than linearly as more choice points are added. Previous approaches to reducing authorial burden for computer RPGs have relied on creating better tools to manage existing unwieldy structures of quests and dialogue trees. We hypothesize that reducing authorial burden and increasing agency are two sides of the same coin, requiring specific advancements in two related areas of design and technology research: (1) dynamic story management architecture that represents story events abstractly and allows story elements to be selected and re-ordered in response to player choices, and (2) dynamic dialogue generation to allow a single story event to be revealed differently by different characters and in the context of dynamic relationships between those characters and the player. This paper describes SpyFeet, a playable prototype of a storytellingsystem designed to test this hypothesis.

A growing issue in the development of realistic and entertain-ing interactive games is the need for mechanisms that support ongoing natural language conversation between human players and artificial non-player characters. Unfortunately, many methods for implementing natural language generation(NLG) induce a significant burden on the author, do not scale well, or require specialized linguistic knowledge. We formalize the notion of typed-templates, an extension of standard structures employed in template-based NLG. We further provide novel algorithms that, when applied to typed-templates, ameliorate the above issues by affording computational support for authoring and increased variation in utterance and scenario generation. We demonstrate the efficacy of typed-templates and the algorithms through a user study.

In this work, we look at the challenge of learning in an action game,Infinite Mario. Learning to play an action game can be divided intotwo distinct but related problems, learning an object-relatedbehavior and selecting a primitive action. We propose a framework that allows for the use of reinforcement learning for both ofthese problems. We present promising results in some instances of thegame and identify some problems that might affect learning.