15,218 research outputs found

    Generating map sketches for strategy games

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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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