Balancing act : the effect of dynamic difficulty adjustment in competitive multiplayer video games

Baldwin, Alexander

doi:10.5204/thesis.eprints.102669

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…This variation stems from the permeability of prior layers; changes in them can be frequent. For example, the needs of a person for agency can vary from one situation to another (e.g., a person may accept full control of an autonomous car on the freeway but not on side roads [Brown & Osborn, 2019]) and the ability of a system to cater to such needs may vary as the needs change (e.g., a videogame may increase a sense of competence until a user reaches a certain level, and afterwards it can become difficult or almost impossible to master, such that system use no longer supports the competency needs of the user [Baldwin, 2017]).…”

Section: A Framework For Studying the Interactions Between Individualmentioning

confidence: 99%

An intertwined perspective on technology and digitised individuals: Linkages, needs and outcomes

Turel

Matt

Trenz

et al. 2020

Information Systems Journal

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Information technology (IT) has changed dramatically over the last several decades. Although, in its early days IT has been mostly used as a tool for conducting business or running complex governmental and organisational operations, it has shifted to also become a productivity and hedonic tool for individual users (Matt, Trenz, Cheung, & Turel, 2019). Readers can reflect on how many technologies surround them now, as they read this article. These can include a range of mixed-use technologies that cater to both hedonic and utilitarian objectives. Examples include, but are not limited to, the device through which this article is read (desktop, laptop, tablet, or smartphone), a smart TV, smart kitchen appliances, smart watches, wearable fitness trackers, and connected or autonomous cars that drive the readers whilst they listen to a text-to-voice generated version of this article. These changes in the technological landscape have presumably been supported by technological advancements that have made technology more connected and affordable than before, smaller, yet broader in its capabilities, beyond merely being job-or leisure-oriented (Turel et al., 2019). Since many of these technologies are used exclusively in leisure or non-work settings, or in both work and non-work (including leisure) settings, they have created what we call digitised individuals, defined as users who use at least one digital technology in their non-work life domains. 1 Note that digitised individuals have existed since the dawn of personal computers, but we see major growth in the last decade with the vast penetration of smartphones, social media and personal lifestyle and health technologies. We view the collective of digitised individuals as contributing to the phenomenon of the digitisation of individuals, defined as the proliferation of digital technologies in the lives of individual users (Matt, Trenz, et al., 2019). Although the bare minimum to qualify as digitised individuals according to this definition is using one technology for non-work including leisure purposes, nowadays, many people use multiple technologies to different extents, being integrated into their lives in many different ways. This creates a large variability in the extent of their digitisation. The combination of the significant diffusion of digital technologies used by individuals with the variability in their (partly joint) usage leads to the necessity to develop an intertwined perspective that considers technology and the individual at the same time, that is, a socio-technical perspective. Several studies have argued for a need to understand digitised individuals, the drivers of the digitisation of individuals and the consequences of this digitisation, because technologies aimed at the digitisation of individuals have unique features that distinguish them from commonly examined business technologies, or that are insufficiently highlighted and understood in studies of non-work technologies (Matt, Trenz, et al., 2019; Turel et al., 2019). These characteristics inclu...

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Section: A Framework For Studying the Interactions Between Individualmentioning

confidence: 99%

An intertwined perspective on technology and digitised individuals: Linkages, needs and outcomes

Turel

Matt

Trenz

et al. 2020

Information Systems Journal

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“…Multiplayer online game Comparing with the console game, multiplayer online game is more complex and More diverse. However, some existing games use more direct methods similar to Dynamic Difficulty Adjustment (DDA) in a multiplayer context [15].…”

Section: A Dynamic Difficulty Adjustment Systemmentioning

confidence: 99%

Machine Learning and Data mining on the innovation of E-sports industry

Chen

Mei

Huang

2020

International Journal of Education and Information Technologies

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AI technology brings many revolutionary innovation opportunities to the e-sports industry. With the help of data mining, we can analyze the advantages and disadvantages of competitors, and predict the trend of the situation in the future. With the help of the agent created by intensive in-depth learning, it can assist players of different levels to carry out routine training, so as to improve the overall activity of the game. With the help of AI's big data advantage, AI can assist E-sports teaching to regard E-sports specialty as an experimental platform for using cutting-edge technology to reform and innovate traditional education and provide forward-looking guidance for future education. This paper uses CNN, LSTM, and LSTM + CNN three model to predict the outcome of the game according to the heroes selected by both teams, and has achieved good prediction results.

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“…Video games that contribute to this development are very successful in environmental design and transferring the realistic behavior of Non-Player Characters (NPC) [1]. In video games, which have become an important part of the entertainment industry, the goal is to optimize players' experience rather than creating the most difficult game [2][3][4]. A good video game has a long learning curve.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Game Levelling

Çetin

Sarica

2020

Balkan Journal of Electrical and Computer Engineering

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The applications of artificial intelligence (AI), which is a comprehensive information technology, have been closely related to game technologies. Today, artificial intelligencebased game development applications are increasing their popularity day by day. In this study, the levelling process of a 2dimensional (2D) platform game has been investigated. The game developed and called "Renga" has a basic gameplay. Game data has been processed through an artificial neural network (ANN), k-nearest neighbour, decision and random tree algorithms and deep learning model that is trained with gameplay and user information. The classification process with the output data provides results for the next game level. In this way, the most effective playability impression that the developers offer to the game users has been created according to game. Furthermore, the variety of difficulty calculated with dynamic data by the user is provided by Renga, in which new sections/levels are created with user-specific assets. Thus, the most efficient gaming experience has been transferred to the users.

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Balancing act : the effect of dynamic difficulty adjustment in competitive multiplayer video games

Cited by 5 publications

References 1 publication

An intertwined perspective on technology and digitised individuals: Linkages, needs and outcomes

An intertwined perspective on technology and digitised individuals: Linkages, needs and outcomes

Machine Learning and Data mining on the innovation of E-sports industry

Artificial Intelligence Based Game Levelling

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