15,218 research outputs found
Generating map sketches for strategy games
How can a human and an algorithm productively collaborate on generating game content? In this paper, we try to answer this question in the context of
generating balanced and interesting low-resolution sketches for game levels. We
introduce six important criteria for successful strategy game maps, and present
map sketches optimized for one or more of these criteria via a constrained evolutionary algorithm. The sketch-based map representation and the computationally
lightweight evaluation methods are geared towards the integration of the evolutionary algorithm within a mixed-initiative tool, allowing for the co-creation of
game content by a human and an artificial designer.peer-reviewe
Searching for good and diverse game levels
Abstract: In procedural content generation, one is often interested in generating a large number of artifacts that are not only of high quality but also diverse, in terms of gameplay, visual impression or some other criterion. We investigate several search-based approaches to creating good and diverse game content, in particular approaches based on evolution strategies with or without diversity preservation mechanisms, novelty search and random search. The content domain is game levels, more precisely map sketches for strategy games, which are meant to be used as suggestions in the Sentient Sketchbook design tool. Several diversity metrics are possible for this type of content: we investigate tile-based, objective-based and visual impression distance. We find that evolution with diversity preservation mechanisms can produce both good and diverse content, but only when using appropriate distance measures. Reversely, we can draw conclusions about the suitability of these distance measures for the domain from the comparison of diversity preserving versus blind restart evolutionary algorithms.peer-reviewe
A Generative Model of People in Clothing
We present the first image-based generative model of people in clothing for
the full body. We sidestep the commonly used complex graphics rendering
pipeline and the need for high-quality 3D scans of dressed people. Instead, we
learn generative models from a large image database. The main challenge is to
cope with the high variance in human pose, shape and appearance. For this
reason, pure image-based approaches have not been considered so far. We show
that this challenge can be overcome by splitting the generating process in two
parts. First, we learn to generate a semantic segmentation of the body and
clothing. Second, we learn a conditional model on the resulting segments that
creates realistic images. The full model is differentiable and can be
conditioned on pose, shape or color. The result are samples of people in
different clothing items and styles. The proposed model can generate entirely
new people with realistic clothing. In several experiments we present
encouraging results that suggest an entirely data-driven approach to people
generation is possible
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
Procedural personas as critics for dungeon generation
This paper introduces a constrained optimization method which uses
procedural personas to evaluate the playability and quality of evolved dungeon
levels. Procedural personas represent archetypical player behaviors, and their
controllers have been evolved to maximize a specific utility which drives their
decisions. A “baseline” persona evaluates whether a level is playable by testing
if it can survive in a worst-case scenario of the playthrough. On the other hand, a
Monster Killer persona or a Treasure Collector persona evaluates playable levels
based on how many monsters it can kill or how many treasures it can collect, respectively.
Results show that the implemented two-population genetic algorithm
discovers playable levels quickly and reliably, while the different personas affect
the layout, difficulty level and tactical depth of the generated dungeons.The research was supported, in part, by the FP7 ICT project C2Learn (project no:
318480) and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe
Sentient sketchbook : computer-assisted game level authoring
We would like to thank the participants of the user survey
for their valuable feedback.This paper introduces Sentient Sketchbook, a tool which
supports a designer in the creation of game levels. Us-
ing map sketches to alleviate designer effort, the tool auto-
mates playability checks and evaluations and visualizes sig-
ni cant gameplay properties. Most importantly, this paper
introduces constrained novelty search via a two-population
paradigm for generating, in real-time, alternatives to the
author's design and evaluates its potential against current
approaches. The paper concludes with a small-scale user
survey during which industry experts interact with Sentient
Sketchbook to design game levels. Results demonstrate the
tool's potential and provide directions for its improvement.peer-reviewe
Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity
This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe
Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity
This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe
Constrained novelty search : a study on game content generation
Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.peer-reviewe
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