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.

Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author's story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors' original intent, thus balancing authorability against robust multiplayer execution.

Pathfinding is important in many applications, including games, robotics and GPS itinerary planning. In games, most pathfinding methods rely on runtime search. Despite numerous enhancements introduced in recent years, runtime search has the disadvantage that, in bad cases, most parts of a map need to be explored, causing a time performance degradation. In this work we explore a significantly different approach to pathfinding, eliminating the need for runtime search. Optimal paths between all pairs of locations are pre-computed. Since straightforward ways to store pre-computed paths are prohibitively expensive even for maps of moderate size, pre-computed data are compressed, reducing the memory requirements dramatically. At runtime, pathfinding is very fast, as it requires visiting only the locations on an optimal path. In each location, a quick computation provides the next move along the optimal path. We demonstrate the effectiveness of this approach on Baldur's Gate game maps. The compression factor reaches two orders of magnitude, bringing the memory requirements down to reasonable values. Compared to A* search, the runtime speedup reaches and even exceeds two orders of magnitude. When averaged over paths of similar cost, the speedup reaches a value of 700 in our experiments.

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum . We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.

We study the problem of learning probabilistic models of high-level strategic behavior in the real-time strategy (RTS) game StarCraft. The models are automatically learned from sets of game logs and aim to capture the common strategic states and decision points that arise in those games. Unlike most work on behavior/strategy learning and prediction in RTS games, our data-centric approach is not biased by or limited to any set of preconceived strategic concepts. Further, since our behavior model is based on the well-developed and generic paradigm of hidden Markov models, it supports a variety of uses for the design of AI players and human assistants. For example, the learned models can be used to make probabilistic predictions of a player's future actions based on observations, to simulate possible future trajectories of a player, or to identify uncharacteristic or novel strategies in a game database. In addition, the learned qualitative structure of the model can be analyzed by humans in order to categorize common strategic elements. We demonstrate our approach by learning models from 331 expert-level games and provide both a qualitative and quantitative assessment of the learned model's utility.

Character intention revision is an essential component of stories, but it has yet to be incorporated into story generation systems. However, intentionality, one component of intention revision, has been explored in both narrative generation and logical formalisms. The IRIS system adopts the belief/desire/intention framework of intentionality from logical formalisms and combines it with preexisting concepts of intentionality in narrative. IRIS also introduces the crucial concept of intention revision for characters in the story. The intent of this synthesis is to create stories with dynamic and believable characters that update their beliefs, replan, and revise their intentions over the course of the story.

Grid representations offer many advantages for path planning. Lookups in grids are fast, due to the uniform memory layout, and it is easy to modify grids. But, grids often have significant memory requirements, they cannot directly represent more complex surfaces, and path planning is slower due to their high granularity representation of the world. The speed of path planning on grids has been addressed using abstract representations, such as has been documented in work on Dragon Age: Origins. The abstract representation used in this game was compact, preventing permanent changes to the grid. In this paper we introduce a sparse grid representation, where grid cells are only stored where necessary. From this sparse representation we incrementally build an abstract graph which represents possible movement in the world at a high-level of granularity. This sparse representation also allows the representation of three-dimensional worlds. This representation allows the world to be incrementally changed in under a millisecond, reducing the maximum memory required to store a map and abstraction from Dragon Age: Origins by nearly one megabyte. Fundamentally, the representation allows previously allocated but unused memory to be used in ways that result in higher-quality planning and more intelligent agents.

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).

Goal recognition is the task of inferring users’ goals from sequences of observed actions. By enabling player-adaptive digital games to dynamically adjust their behavior in concert with players’ changing goals, goal recognition can inform adaptive decision making for a broad range of entertainment, training, and education applications. This paper presents a goal recognition framework based on Markov logic networks (MLN). The model’s parameters are directly learned from a corpus of actions that was collected through player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with multiple solution paths.