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

Co-creating game content using an adaptive model of user taste

Mixed-initiative procedural content generation can augment and assist human creativity by allowing the algorithm to take care of the mechanisable parts of content creation, such as consistency and playability checking. But it can also enhance human creativity by suggesting new directions and structures, which the designer can choose to adopt or not.peer-reviewe

Evaluation of a Recommender System for Assisting Novice Game Designers

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase users' productivity. We conducted quantitative studies that showed the recommender system increases users' levels of accuracy and computational affect, and decreases their levels of workload.Comment: The 15th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 19

A constructive approach for the generation of underwater environments

This paper introduces Coralize, a library of generators for marine organisms such as corals and sponges. Using constructive algorithms, Coralize can generate stony corals via L-system grammars, soft corals via leaf venation algorithms and sponges via nutrient-based mesh growth. The generative algorithms are parameterizable, allowing a user to adjust the parameters in order to create visually appealing 3D meshes. Such meshes can be used to automatically populate a seabed or reef, in order to create a biologically realistic and aesthetically pleasing underwater environment.The research was supported, in part, by the FP7 ICT projects C2Learn (project no: 318480) and ILearnRW (project no: 318803), and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

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

Enhancements to constrained novelty search : two-population novelty search for generating game content

Novelty search is a recent algorithm geared to explore search spaces without regard to objectives; minimal criteria novelty search is a variant of this algorithm for constrained search spaces. For large search spaces with multiple constraints, however, it is hard to find a set of feasible individuals that is both large and diverse. In this paper, we present two new methods of novelty search for constrained spaces, Feasible-Infeasible Novelty Search and Feasible-Infeasible Dual Novelty Search. Both algorithms keep separate populations of feasible and infeasible individuals, inspired by the FI-2pop genetic algorithm. These algorithms are applied to the problem of creating diverse and feasible game levels, representative of a large class of important problems in procedural content generation for games. Results show that the new algorithms under certain conditions can produce larger and more diverse sets of feasible strategy game maps than existing algorithms. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. It is also shown that the proposed enhancement of offspring boosting increases performance in all cases.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 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

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