In this paper, we apply the idea of testing games by learning interactions with them that cause unwanted behavior of the game to test the competition entries for some of the scenarios of the 2010 StarCraft AI competition. By extending the previously published macro action concept to include macro action sequences for individual game units, by adjusting the concept to the real-time requirements of StarCraft, and by using macros involving specific abilities of game units, our testing system was able to find either weaknesses or system crashes for all of the competition entries of the chosen scenarios. Additionally, by requiring a minimal margin with respect to surviving units, we were able to clearly identify the weaknesses of the tested AIs.

Path planning is a critical part of modern computer games; rare is the game where nothing moves and path planning is unneeded. A* is the workhorse for most path planning applications. Block A* is a state-of-the-art algorithm that is always faster than A* in experiments using game maps. Unlike other methods that improve upon A*'s performance, Block A* is never worse than A* nor require any knowledge of the map. In our experiments, Block A* is ideal for games with randomly generated maps, large maps, or games with a highly dynamic multi-agent environment. Furthermore, in the domain of grid-based any-angle path planning, we show that Block A* is an order of magnitude faster than the previous best any-angle path planning algorithm, Theta*. We empirically show our results using maps from Dragon Age: Origins and Starcraft. Finally, we introduce ``populated game maps'' as a new test bed that is a better approximation of real game conditions than the standard test beds of this field. The main contributions of this paper is a more rigorous set of experiments for Block A*, and introducing a new test bed (populated game maps) that is a more accurate representation of actual game conditions than the standard test beds.

Massive Multiplayer Online Strategy games present several unique challenges to players and designers. There is the need to constantly adapt to changes in the game itself and the need to achieve a certain level of simulation and realism, which typically implies battles involving combat with several distinct armies, combat phases and diferent terrains; resource management which involves buying and selling goods and combining lots of diferent kinds of resources to fund the player's nation and cutthroat diplomacy which dictates the pace of the game. However, these constant changes and simulation mechanisms make a game harder to play, increasing the amount of effort required to play it properly. As some of these games take months to be played, players who become inactive have a negative impact on the game. This work pretends to demonstrate how to create versatile agents for playing Massive Multiplayer Online Turn Based Strategy Games, while keeping close attention to their playing performance. In a test to measure this performance the results showed similar survival performance between humans and AIs.

Conflict is an essential element of interesting stories, but little research in computer narrative has addressed it directly. We present a model of narrative conflict inspired by narratology research and based on Partial Order Causal Link (POCL) planning. This model informs an algorithm called CPOCL which extends previous research in story generation. Rather than eliminate all threatened causal links, CPOCL marks certain steps in a plan as non-executed in order to preserve the conflicting subplans of all characters without damaging the causal soundness of the overall story.

Game metrics are an approach to understanding games and gameplay by analyzing and visualizing information collected from players in playtests. This paper proposes that another source of metrics is the game itself, and that not all information needs to (or ought to) come from empirical playtests. I discuss seven strategies for extracting information from games, and discuss how the information retrieved in this manner relates to empirical playtest metrics---which it differs from but can often complement.

Text annotations are of great use to researchers in the language sciences, and much effort has been invested in creating annotated corpora for an wide variety of purposes. Unfortunately, software support for these corpora tends to be quite limited: it is usually ad-hoc, poorly designed and documented, or not released for public use. I describe an annotation tool, the Story Workbench, which provides a generic platform for text annotation. It is free, open-source, cross-platform, and user friendly. It provides a number of common text annotation operations, including representations (e.g., tokens, sentences, parts of speech), functions (e.g., generation of initial annotations by algorithm, checking annotation validity by rule, fully manual manipulation of annotations) and tools (e.g., distributing texts to annotators via version control, merging doubly-annotated texts into a single file). The tool is extensible at many different levels, admitting new representations, algorithm, and tools. I enumerate ten important features and illustrate how they support the annotation process at three levels: (1) annotation of individual texts by a single annotator, (2) double-annotation of texts by two annotators and an adjudicator, and (3) annotation scheme development. The Story Workbench is scheduled for public release in March 2012.

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.

Online multiplayer gaming has emerged as a popular form of entertainment. the course of a multiplayer game, playerinteractions may result in interesting emer- gent narratives that go unnoticed. Afterthought is a system that monitors player activity, recognizes instances of story elements in gameplay and renders cinematic highlights of the story-oriented game play, allowing players to view these emergent narratives after completing their gameplay session. This paper describes Afterthought’s implementation as well as an empirical human-subjects evaluation of the effectiveness of the cinematics that it creates.

This paper presents a search-based approach to generating game content that satisfies both gameplay requirements and user-expressed aesthetic criteria. Using evolutionary constraint satisfaction, we search for spaceships (for a space combat game) represented as compositional pattern-producing networks. While the gameplay requirements are satisfied by ad-hoc defined constraints, the aesthetic evaluation function can also be informed by human aesthetic judgement. This is achieved using indirect interactive evolution, where an evaluation function re-weights an array of aesthetic criteria based on the choices of a human player. Early results show that we can create aesthetically diverse and interesting spaceships while retaining in-game functionality.

Empirical research supporting computational models of narrative is often constrained by the lack of large-scale corpora with deep annotation. In this paper, we report on our annotation and analysis of a dataset of 283 individual narrations of the events in two short video clips. The utterances in the narrative transcripts were annotated to align with known events in the source videos, offering a unique opportunity to study the regularities and variations in the way that different people describe the exact same set of events. We identified the causal relationships between events in the two video clips, and investigated the role that causality plays in determining whether subjects will mention a particular story event and the likelihood that these events will be told in the order that they occurred in the original videos.