Adapting models of visual aesthetics for personalized content creation

This paper introduces a search-based approach to personalized content generation with respect to visual aesthetics. The approach is based on a two-step adaptation procedure where (1) the evaluation function that characterizes the content is adjusted to match the visual aesthetics of users and (2) the content itself is optimized based on the personalized evaluation function. To test the efficacy of the approach we design fitness functions based on universal properties of visual perception, inspired by psychological and neurobiological research. Using these visual properties we generate aesthetically pleasing 2D game spaceships via neuroevolutionary constrained optimization and evaluate the impact of the designed visual properties on the generated spaceships. The offline generated spaceships are used as the initial population of an interactive evolution experiment in which players are asked to choose spaceships according to their visual taste: the impact of the various visual properties is adjusted based on player preferences and new content is generated online based on the updated computational model of visual aesthetics of the player. Results are presented which show the potential of the approach in generating content which is based on subjective criteria of visual aesthetics.Thanks to all the participants of the interactive evolution experiement. The research was supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the Danish Research Agency, Ministry of Science, Technology and Innovation project AGameComIn; project number: 274- 09-0083.peer-reviewe

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

Adaptive game level creation through rank-based interactive evolution

This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.The research is supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the FP7 ICT project C2Learn (project no: 318480).peer-reviewe

Designer modeling for personalized game content creation tools

With the growing use of automated content creation and computer-aided design tools in game development, there is potential for enhancing the design process through personalized interactions between the software and the game developer. This paper proposes designer modeling for capturing the designer’s preferences, goals and processes from their interaction with a computer- aided design tool, and suggests methods and domains within game development where such a model can be applied. We describe how designer modeling could be integrated with current work on automated and mixed- initiative content creation, and envision future directions which focus on personalizing the processes to a designer’s particular wishes.peer-reviewe

Responsive and Personalized Web Layouts with Integer Programming

Over the past decade, responsive web design (RWD) has become the de facto standard for adapting web pages to a wide range of devices used for browsing. While RWD has improved the usability of web pages, it is not without drawbacks and limitations: designers and developers must manually design the web layouts for multiple screen sizes and implement associated adaptation rules, and its "one responsive design fits all"approach lacks support for personalization. This paper presents a novel approach for automated generation of responsive and personalized web layouts. Given an existing web page design and preferences related to design objectives, our integer programming -based optimizer generates a consistent set of web designs. Where relevant data is available, these can be further automatically personalized for the user and browsing device. The paper includes presentation of techniques for runtime adaptation of the designs generated into a fully responsive grid layout for web browsing. Results from our ratings-based online studies with end users (N = 86) and designers (N = 64) show that the proposed approach can automatically create high-quality responsive web layouts for a variety of real-world websites.Peer reviewe

Designer modeling for sentient sketchbook

This paper documents the challenges in creating a computer-aided level design tool which incorporates computergenerated suggestions which appeal to the human user. Several steps are suggested in order to make the suggestions more appropriate to a specific user’s overall style, current focus, and end-goals. Designer style is modeled via choice-based interactive evolution which adapts the impact of different dimensions of quality based on the designer’s choice of certain suggestions over others. Modeling process is carried out similarly to style, but adapting to the current focus of the designer’s actions. Goals are modeled by estimating the visual patterns of the designer’s final artifact and changing the parameters of the algorithm to enforce such patterns on generated suggestions.The research is supported, in part, by the FP7 ICT project C2Learn (project no: 318480) and the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

Game AI revisited

More than a decade after the early research efforts on the use of artificial intelligence (AI) in computer games and the establishment of a new AI domain the term “game AI” needs to be redefined. Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation and pathfinding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would significantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a significant potential for the design of better games. This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI field and evidently put the game AI term under a new perspective. These game AI flagship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities.peer-reviewe

Webpage design optimization using genetic algorithm driven CSS

In the rapid emergence of globalization, e-commerce, and internet accessibility in remote parts of the world, ongoing feedback and participation from site visitors are essential for attaining clear and effective communication on a web site. This thesis presents a computational experiment for optimizing design of a webpage in an evolutionary manner. Webpage personalization is viewed as a configuration problem whose goal is to determine the optimal presentation of a webpage while taking into account the preference of the web author (designer), layout constraints (web design/editing language: HTML, CSS), and viewer interaction with the browser. The study proposes use of genetic algorithm-driven Cascading Style Sheets (CSS) to assist the process of webpage design optimization. This method will engage visitors to remotely modify and enhance the style (type, layout and color) of web site to fit their aesthetic and functional representation of well-received design. The preference feedback from user will be stored in an application server for automated evolutionary selection process and reinitialized for the next generation of users. Through the experimentation of web prototype and user evaluation test, the implementation of this method is examined and the derived design solutions are analyzed based on web aesthetics, standards, and accessibility

Boosting computational creativity with human interaction in mixed-initiative co-creation tasks

Research in computational creativity often focuses on autonomously creative systems, which incorporate creative processes and result in creative outcomes. However, the integration of artificially intelligent processes in human-computer interaction tools necessitates that we identify how computational creativity can be shaped and ultimately enhanced by human intervention. This paper attempts to connect mixed-initiative design with established theories of computational creativity, and adapt the latter to accommodate a human initiative impacting computationally creative processes and outcomes. Several case studies of mixed-initiative tools for design and play are used to corroborate the arguments in this paper.peer-reviewe