Automation of play:theorizing self-playing games and post-human ludic agents

This article offers a critical reflection on automation of play and its significance for the theoretical inquiries into digital games and play. Automation has become an ever more noticeable phenomenon in the domain of video games, expressed by self-playing game worlds, self-acting characters, and non-human agents traversing multiplayer spaces. On the following pages, the author explores various instances of automated non-human play and proposes a post-human theoretical lens, which may help to create a new framework for the understanding of videogames, renegotiate the current theories of interaction prevalent in game studies, and rethink the relationship between human players and digital games

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

Automated state of play: rethinking anthropocentric rules of the game

Automation of play has become an ever more noticeable phenomenon in the domain of video games, expressed by self-playing game worlds, self-acting characters, and non-human agents traversing multiplayer spaces. This article proposes to look at AI-driven non-human play and, what follows, rethink digital games, taking into consideration their cybernetic nature, thus departing from the anthropocentric perspectives dominating the field of Game Studies. A decentralised post-humanist reading, as the author argues, not only allows to rethink digital games and play, but is a necessary condition to critically reflect AI, which due to the fictional character of video games, often plays by very different rules than the so-called “true” AI

No Grice: Computers that Lie, Deceive and Conceal

In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud \ud Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud \ud This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications

Analysis of human-computer interaction time series using Deep Learning

Dissertação de mestrado integrado em Engenharia InformáticaThe collection and use of data resulting from human-computer interaction are becoming more and more common. These have been allowing for the birth of intelligent systems that extract powerful knowledge, potentially improving the user experience or even originating various digital services. With the rapid scientific advancements that have been taking place in the field of Deep Learning, it is convenient to review the underlying techniques currently used in these systems. In this work, we propose an approach to the general task of analyzing such interactions in the form of time series, using Deep Learning. We then rely on this approach to develop an anti-cheating system for video games using only keyboard and mouse input data. This system can work with any video game, and with minor adjustments, it can be easily adapted to new platforms (such as mobile and gaming consoles). Experiments suggest that analyzing HCI time series data with deep learning yields better results while providing solutions that do not rely highly on domain knowledge as traditional systems.A recolha e a utilização de dados resultantes da interação humano-computador estão a tornar-se cada vez mais comuns. Estas têm permitido o surgimento de sistemas inteligentes capazes de extrair conhecimento ex tremamente útil, potencialmente melhorando a experiência do utilizador ou mesmo originando diversos serviços digitais. Com os acelerados avanços científicos na área do Deep Learning, torna-se conveniente rever as técni cas subjacentes a estes sistemas. Neste trabalho, propomos uma abordagem ao problema geral de analisar tais interações na forma de séries temporais, utilizando Deep Learning. Apoiamo-nos então nesta abordagem para desenvolver um sistema de anti-cheating para videojogos, utilizando apenas dados de input de rato e teclado. Este sistema funciona com qualquer jogo e pode, com pequenos ajustes, ser adaptado para novas plataformas (como dispositivos móveis ou consolas). As experiências sugerem que analisar dados de séries temporais de interação humano-computador pro duz melhores resultados, disponibilizando soluções que não são altamente dependentes de conhecimento de domínio como sistemas tradicionais

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Tag is a popular children’s playground game. It revolves around taggers that chase and then tag runners, upon which their roles switch. There are many variations of the game that aim to keep children engaged by presenting them with challenges and different types of gameplay. We argue that the introduction of sensing and floor projection technology in the playground can aid in providing both variation and challenge. To this end, we need to understand players’ behavior in the playground and steer the interactions using projections accordingly. In this paper, we first analyze the behavior of taggers and runners in a traditional tag setting. We focus on behavioral cues that differ between the two roles. Based on these, we present a probabilistic role recognition model. We then move to an interactive setting and evaluate the model on tag sessions in an interactive tag playground. Our model achieves 77.96 % accuracy, which demonstrates the feasibility of our approach. We identify several avenues for improvement. Eventually, these should lead to a more thorough understanding of what happens in the playground, not only regarding player roles but also when the play breaks down, for example when players are bored or cheat