67,508 research outputs found
Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game
Social media has become a major communication channel for communities
centered around video games. Consequently, social media offers a rich data
source to study online communities and the discussions evolving around games.
Towards this end, we explore a large-scale dataset consisting of over 1 million
tweets related to the online multiplayer shooter Destiny and spanning a time
period of about 14 months using unsupervised clustering and topic modelling.
Furthermore, we correlate Twitter activity of over 3,000 players with their
playtime. Our results contribute to the understanding of online player
communities by identifying distinct player groups with respect to their Twitter
characteristics, describing subgroups within the Destiny community, and
uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201
From individual characters to large crowds: augmenting the believability of open-world games through exploring social emotion in pedestrian groups
Crowds of non-player characters improve the game-play experiences of open-world video-games. Grouping is a common phenomenon of crowds and plays an important role in crowd behaviour. Recent crowd simulation research focuses on group modelling in pedestrian crowds and game-designers have argued that the design of non-player characters should capture and exploit the relationship between characters. The concepts of social groups and inter-character relationships are not new in social psychology, and on-going work addresses the social life of emotions and its behavioural consequences on individuals and groups alike. The aim of this paper is to provide an overview of current research in social psychology, and to use the findings as a source of inspiration to design a social network of non-player characters, with application to the problem of group modelling in simulated crowds in computer games
MeGARA: Menu-based Game Abstraction and Abstraction Refinement of Markov Automata
Markov automata combine continuous time, probabilistic transitions, and
nondeterminism in a single model. They represent an important and powerful way
to model a wide range of complex real-life systems. However, such models tend
to be large and difficult to handle, making abstraction and abstraction
refinement necessary. In this paper we present an abstraction and abstraction
refinement technique for Markov automata, based on the game-based and
menu-based abstraction of probabilistic automata. First experiments show that a
significant reduction in size is possible using abstraction.Comment: In Proceedings QAPL 2014, arXiv:1406.156
Wind generator behaviour in a pay-as-bid curtailment market
A pay-as-bid curtailment market, where Wind Power Plants (WPPs) may offer prices to have their output reduced in the event of network balancing or stability constraints, is one approach towards the market integration of a high proportion of wind energy onto a power system. Such a market aims to procure curtailment at a cost close to the marginal value of the electricity plus renewable subsidies and incentives, reducing risks for WPPs while minimising costs to the Independent System Operator (ISO). Through the use of game theory and market modelling, a key set of bidding strategies are identified that may evolve within such a market, which may act in opposition to the goals of the ISO. These are applied to a variety of network conditions in order to determine their likely impact and the resulting bidding signals provided to market participants. Bidding behaviours and market fluidity may also be affected by factors particular to wind power plants. Through analysis of both ex ante and ex post case studies, the existence of these behaviours is demonstrated, illustrating that a pay-as-bid curtailment market may not be efficient at price discovery in practice
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