Refining the paradigm of sketching in AI-based level design

This paper describes computational processes which can simulate how human designers sketch and then iteratively refine their work. The paper uses the concept of a map sketch as an initial, low-resolution and low-fidelity prototype of a game level, and suggests how such map sketches can be refined computationally. Different case studies with map sketches of different genres showcase how refinement can be achieved via increasing the resolution of the game level, increasing the fidelity of the function which evaluates it, or a combination of the two. While these case studies use genetic algorithms to automatically generate levels at different degrees of refinement, the general method described in this paper can be used with most procedural generation methods, as well as for AI-assisted design alongside a human creator.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 Auto- GameDesign (project no: 630665).peer-reviewe

Feature selection for capturing the experience of fun

Several approaches for constructing metrics to capture player experience have been presented previously. In this paper, we propose a generic methodology based on feature selection and preference machine learning for constructing such metric models of the degree to which a player enjoys a given game. For that purpose, previous and new survey experiments on computer and physical interactive games are presented. Given effective data collection a set of numerical features is extracted from a player’s interaction with the game and its physiological state. Then feature selection algorithms are employed together with a function approximator based on artificial neural networks to construct feature sets and function that model the players’ notion of ‘fun’ for the game under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those ‘fun’ (entertainment) preferences expressed by the subjects. The results show that effective models can be constructed using the proposed approach. The limitations and the use of the methodology as an effective adaptive mechanism to entertainment augmentation are discussed.This work was supported in part by the Danish Research Agency, Ministry of Science, Technology and Innovation (project no: 274-05-0511).peer-reviewe

Towards a generic method of evaluating game levels

This paper addresses the problem of evaluating the quality of game levels across different games and even genres, which is of key importance for making procedural content generation and assisted game design tools more generally applicable. Three game design patterns are identified for having high generality while being easily quantifiable: area control, exploration and balance. Formulas for measuring the extent to which a level includes these concepts are proposed, and evaluation functions are derived for levels in two different game genres: multiplayer strategy game maps and single-player roguelike dungeons. To illustrate the impact of these evaluation functions, and the similarity of impact across domains, sets of levels for each function are generated using a constrained genetic algorithm. The proposed measures can easily be extended to other game genres.This research was supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the FP7 ICT project C2Learn (project no: 318480).peer-reviewe

Multi-level evolution of shooter levels

This paper introduces a search-based generative process for first person shooter levels. Genetic algorithms evolve the level’s architecture and the placement of powerups and player spawnpoints, generating levels with one floor or two floors. The evaluation of generated levels combines metrics collected from simulations of artificial agents competing in the level and theory-based heuristics targeting general level design patterns. Both simulation-based and theory-driven evaluations target player balance and exploration, while resulting levels emergently exhibit several popular design patters of shooter levels.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

Targeting horror via level and soundscape generation

Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player’s tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them.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 Auto- GameDesign (project no: 630665).peer-reviewe

Investigating the interplay between camera viewpoints, game information, and challenge

Players perceive information about game environments through a virtual camera. While a significant discussion in the industry and in academic research circles has centered around effective camera control, it is focused mainly on occlusion free placement and smooth movement. The relationship between information communicated by the camera about game state and the selection of camera parameters has not been investigated. In this paper, we systematically investigate the effect of different camera profiles on player experience in a 3D prey/predator test-bed game. We describe a constraint-based dynamic camera system that maintains the position and orientation of the camera based on the constraints imposed by given camera profiles. The impact of different profiles on the amount of game information provided to the player and the player's game challenge preferences is investigated through a user experiment. An artificial neural network model of challenge constructed using artificial evolution reveals the nonlinear mapping between challenge and information features.peer-reviewe

A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft

The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.Comment: 7 pages; Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2011), Palo Alto : \'Etats-Unis (2011

Automated Game Design Learning

While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201