Generation and Analysis of Content for Physics-Based Video Games

The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations. The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective. While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications

AdSplit: Separating smartphone advertising from applications

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the advertiser. Advertising libraries require additional permissions, resulting in additional permission requests to users. Likewise, a malicious application could simulate the behavior of the advertising library, forging the user's interaction and effectively stealing money from the advertiser. This paper describes AdSplit, where we extended Android to allow an application and its advertising to run as separate processes, under separate user-ids, eliminating the need for applications to request permissions on behalf of their advertising libraries. We also leverage mechanisms from Quire to allow the remote server to validate the authenticity of client-side behavior. In this paper, we quantify the degree of permission bloat caused by advertising, with a study of thousands of downloaded apps. AdSplit automatically recompiles apps to extract their ad services, and we measure minimal runtime overhead. We also observe that most ad libraries just embed an HTML widget within and describe how AdSplit can be designed with this in mind to avoid any need for ads to have native code

Social, Casual and Mobile Games

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Social, casual and mobile games, played on devices such as smartphones, tablets, or PCs and accessed through online social networks, have become extremely popular, and are changing the ways in which games are designed, understood, and played. These games have sparked a revolution as more people from a broader demographic than ever play games, shifting the stereotype of gaming away from that of hardcore, dedicated play to that of activities that fit into everyday life. Social, Casual and Mobile Games explores the rapidly changing gaming landscape and discusses the ludic, methodological, theoretical, economic, social and cultural challenges that these changes invoke. With chapters discussing locative games, the new freemium economic model, and gamer demographics, as well as close studies of specific games (including Candy Crush Saga, Angry Birds, and Ingress), this collection offers an insight into the changing nature of games and the impact that mobile media is having upon individuals and societies around the world

The Life and Work of George Dombek

George Dombek is an accomplished artist who graduated from the University of Arkansas with degrees in Architecture and Painting. His career as both professor and painter has taken him around the world and produced a substantial body of work. This work seeks to examine his art in a larger art historical context and consider the development of the subjects and style of his paintings over the span of his career

Into the Machine

A thesis presented to the faculty of the Caudill College of Humanities at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Arts by Scarlett Stewart on November 26, 2002

Uncovering the Corporate Brand Identity: A case study on Rovio Entertainment

Thesis purpose: To identify and describe the corporate brand identity of a successful company from the mobile gaming industry, examining its corporate brand development at different growth stages. Methodology: This thesis assumes a constructionist ontological stance and interpretivist epistemological position. We chose an exploratory, qualitative single case study approach where semi-structured in-depth interviews were carried out in addition to email interviews. We also took advantage of secondary data in the form of previous interviews, conference speeches and gathering information from company press releases, company’s website(s) and official blog. Theoretical perspective: The thesis is based upon a theory consisting of literature and academic articles within the fields of branding, product brand identity, corporate brands, corporate brand identity and corporate brand building from a SME context. Empirical data: The presentation of findings is in the form of business history, represented as a timeline. The empirical data has been collected through primary data of three semi-structured and in-depth interviews, and two email interviews. Additional qualitative data in the form of secondary data has been gathered from company press releases, past interviews, company’s website(s) and official blog. Conclusion: We conclude that Rovio Entertainment is a corporate brand, whereas predecessor Relude was not and Rovio Mobile only in later stage developed a weak, yet hidden corporate brand. We also conclude that the corporate brand identity has grown and been influenced alongside Rovio flagship product brand ‘Angry Birds’. We find that it is only recently that the Rovio corporate brand identity has become developed and diverged from the Angry Birds brand

The imperfect observer: Mind, machines, and materialism in the 21st century

The dualist / materialist debates about the nature of consciousness are based on the assumption that an entirely physical universe must ultimately be observable by humans (with infinitely advanced tools). Thus the dualists claim that anything unobservable must be non-physical, while the materialists argue that in theory nothing is unobservable. However, there may be fundamental limitations in the power of human observation, no matter how well aided, that greatly curtail our ability to know and observe even a fully physical universe. This paper presents arguments to support the model of an inherently limited observer and explores the consequences of this view

Models of ICT Innovation. A Focus on the Cinema Sector

The report starts by looking at the competing and overlapping definitions of creative industries, media and content industries. Chapter 1 investigates the fate of R&D and innovation in the creative industries and in the broader Telecom Media and Technology sectors. Chapter 2 summarizes past studies on innovation in distinct media and content industries (videogames, music recording and newspapers publishing) and draws some lessons from them. Chapter 3 delves more deeply into the specific case of cinema. This chapter investigates the film industry's complex and evolving relationship with technologies and technological inventions. Chapter 4 offers a short cross-comparison with R&D in the book publishing industry. Chapter 5 deals with policy issues triggered by the observed digital changes. Chapter 6 concludes with a brief assessment of EU strengths and weaknesses, and offers some recommendations.JRC.J.3-Information Societ

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author