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检索条件"任意字段=Proceedings of the The third workshop on Procedural Content Generation in Games"

共 52 条记录，以下是11-20 订阅

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Tackling generation of combat encounters in role-playing digital games 21

Tackling generation of combat encounters in role-playing dig...

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21st Conference Information Technologies - Applications and Theory, ITAT 2021

作者： Kozma, Matouš Černý, Vojtěch Gemrot, Jakub Faculty of Mathematics and Physics Charles University Ke Karlovu 3 Praha 2121 16 Czech Republic

procedural content generation (PCG) has been used in digital games since the early 1980s. Here we focus on a new problem of generating personalized combat encounters in role playing video games (RPG). A game should provide a player with combat encounters of adequate difficulties, which ideally should be matching the player’s performance in order for a game to provide adequate challenge to the player. In this paper, we describe our own reinforcement learning algorithm that estimates difficulties of combat encounters during game runtime, which can be them used to find next suitable combat encounter of desired difficulty in a stochastic hill-climbing manner. After a player finishes the encounter, its result is propagated through the matrix to update the estimations of not only the presented combat encounter, but also similar ones. To test our solution, we conducted a preliminary study with human players on a simplified RPG game we have developed. The data collected suggests our algorithm can adapt the matrix to the player performance fast from little amounts of data, even though not precisely. Copyright © 2021 for this paper by its authors.

关键词： Matrix algebra

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procedural generation for Divination and Inspiration 20

Procedural Generation for Divination and Inspiration

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15th International Conference on the Foundations of Digital games

作者： Pichlmair, Martin Putney, Charlene IT Univ Copenhagen Ctr Comp Games Res Copenhagen Denmark

ISBN: (纸本)9781450388078

This paper presents a series of experiments that map the expressive space of specific procedural generation techniques with playful aleatory interventions. They are tools for inspiration based on divinatory practices. This paper connects these ancient procedural techniques to contemporary technologies like Twitter bots. We challenge the limits of these technologies in order to playfully explore the role they can play in everyday life. The first experiment, Nostrandomus, remixes ancient prophecies. The second one, Five Sparrows on a Vampire, generates proposals for dining experiences featuring recipes and accompanying eating instructions. The third tool, Haikookies, is a self-help inspired twitter bot that shares fortune cookie-style wisdom in haiku form. The final experiment, Tiphareth, is a set of partially procedurally generated tarot cards. Additionally, Ephemerald, a tool for streamlined Tracery-based [6] procedural content generation is introduced. The takeaway of this paper is that data curation is a fundamental component of working with generative systems. In other words, the human aspect needs to be present in order to create meaningful results.

关键词： procedural content generation text generation design inspiration cooking divination

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Precomputing player movement in platformers for level generation with reachability constraints

Precomputing player movement in platformers for level genera...

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2020 Joint Artificial Intelligence and Interactive Digital Entertainment workshops, AIIDE-WS 2020

作者： Lee, Vivian Partlan, Nathan Cooper, Seth Northeastern University United States

procedural content generation via Machine Learning (PCGML) creates game levels from examples. PCGML for platformers with physics-based player movement generally has not guaranteed the reachability of goals, relying on post-generation filtering approaches or game-specific heuristic rules. In contrast, constraint-based PCGML can provide gameplay guarantees, but has typically been applied to games with simple grid-based movement. In this work, we present a constraint-based PCGML approach for platformers with physics-based player movement that guarantees playability and provides design controllability. Our approach exhaustively precomputes all possible player movement states in example tile-based platformer levels. It extracts metatiles containing local state information and uses them in constraint-based level generation that ensures legal tile neighbors and consistent movement state transitions. The approach can ensure constraints on gameplay, such as the reachability of goals, guaranteeing level playability, and other elements like platforms and bonuses. It can also incorporate other designer-controllable constraints. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

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procedural 3D terrain generation using generative adversarial networks 11

Procedural 3D terrain generation using generative adversaria...

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workshops of the 11th EETN Conference on Artificial Intelligence, SETN 2020 workshops

作者： Panagiotou, Emmanouil Charou, Eleni School of Electrical and Computer Engineering National Technical University of Athens Greece

procedural 3D Terrain generation has become a necessity in open world games, as it can provide unlimited content, through a functionally infinite number of different areas, for players to explore. In our approach, we use Generative Adversarial Networks (GAN) to yield realistic 3D environments based on the distribution of remotely sensed images of landscapes, captured by satellites or drones. Our task consists of synthesizing a random but plausible RGB satellite image and generating a corresponding Height Map in the form of a 3D point cloud that will serve as an appropriate mesh of the landscape. For the first step, we utilize a GAN trained with satellite images that manages to learn the distribution of the dataset, creating novel satellite images. For the second part, we need a one-to-one mapping from RGB images to Digital Elevation Models (DEM). We deploy a Conditional Generative Adversarial network (CGAN), which is the state-of-the-art approach to image-to-image translation, to generate a plausible height map for every randomly generated image of the first model. Combining the generated DEM and RGB image, we are able to construct 3D scenery consisting of a plausible height distribution and colorization, in relation to the remotely sensed landscapes provided during training. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Generative adversarial networks

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Towards a model to meet players’ preferences in games 3

Towards a model to meet players’ preferences in games

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3rd workshop on games-Human Interaction, GHItaly 2019

作者： Bellini, Mattia University of Milan Milan Italy

Different have been the attempts to use procedural content generation via Machine Learning in game development. Among the others, some researchers have tried to adapt a game, or some part of it, to the user playing it. This approach has been called "adaptive game design". Contrarily to what it may seem, apparently the most interesting findings in this field have been made for drama managers, i.e. for the artificial intelligences that procedurally generate story flow. The paper takes the move from what seems to be a missing in current literature and it is aimed at proposing and discussing a possible procedural content generation via machine learning model that takes the latest approaches in machine learning applied to drama managers and combine them with findings from adaptive game design. The objective of the proposed model is to give players the best possible gaming experience of a highly branched game, depending on their attitudes towards the gaming world. Copyright © 2019 for this paper by its authors.

关键词： Machine learning

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Presenting GHItaly19-3rd workshop on games-Human Interaction 19

Presenting GHItaly19-3rd Workshop on Games-Human Interaction

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13th Biannual Conference of the Italian-SIGCHI-Chapter on Designing the Next Interaction (CHItaly)

作者： De Marsico, Maria Gadia, Davide Maggiorini, Dario Mariani, Ilaria Ripamonti, Laura Anna Sapienza Univ Rome Rome Italy Univ Milan Milan Italy Politecn Milan Milan Italy

ISBN: (纸本)9781450371902

This short paper introduces the contributions accepted for the 3rd workshop on games-Human Interaction - GHItaly19, held in connection with CHItaly 2019. Since its first edition, this series of workshops aims at focusing on every aspect of the multidisciplinary process of design and development of game interfaces. The quality of the resulting User eXperience is a highly relevant goal to achieve, in order to engage and satisfy the target users, especially when multidimensional artifacts such as video games are concerned. An important consideration concerns the multifaceted range of applications of video games, that has long overcome the borders of amusement.

关键词： HCI Game design Usability Brain Computer Interface Adaptation Affective computing Machine Learning procedural content generation Social interaction Interaction Design Information visualization Player experience Storytelling Gamification

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Blending levels from different games using LSTMs

Blending levels from different games using LSTMs

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2018 Joint of the Artificial Intelligence and Interactive Digital Entertainment workshops, AIIDE-WS 2018

作者： Sarkar, Anurag Cooper, Seth Northeastern University BostonMA United States

Recent work has shown machine learning approaches to be effective in training models on existing game data for informing procedural generation of novel content. Specifically, ML techniques have successfully used existing game levels to train models for generating new levels and blending existing ones. Such blending of not just levels, but entire games, has also been proposed as a possible means of generating new games. In this paper, we build on these works by training Long Short Term Memory recurrent neural networks on level data for two separate platformer games—Super Mario Bros. and Kid Icarus—and use these models to create new levels that are blends of levels from both games, thus creating a level generator for a novel, third game. © 2018 CEUR-WS. All Rights Reserved.

关键词： Blending

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Wonderful design: Applying appraisal theory to procedural level generation

Wonderful design: Applying appraisal theory to procedural le...

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2018 Joint of the Artificial Intelligence and Interactive Digital Entertainment workshops, AIIDE-WS 2018

作者： Corstjens, Rosa Bouwer, Anders Dormans, Joris Van Rozen, Riemer Amsterdam University of Applied Sciences Amsterdam Netherlands Ludomotion Amsterdam Netherlands Centrum Wiskunde and Informatica Amsterdam Netherlands

procedural level generation for games is an active field of research with successful applications. However, how to generate content that embodies design intent is still an open research question. Level designers lack abstractions and tools for authoring generated artifacts for affecting emotion. We propose a novel pattern language for generative level design inspired by Appraisal Theory. Its patterns enable designers to add meaning, depth, and cohesiveness to the resulting content, and modify artifacts to make the content more engaging. We illustrate how these patterns can be implemented in a generative grammar for level generation for an adventure game. Formative evaluation of generated level content demonstrates the feasibility of the approach and suggests points for further improvements. Future work could focus on other elements which seem important for affecting emotions, including pacing, perception, and expectation. © 2018 CEUR-WS. All Rights Reserved.

关键词： Behavioral research

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Leveraging Multi-Layer Level Representations for Puzzle-Platformer Level generation 13

Leveraging Multi-Layer Level Representations for Puzzle-Plat...

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13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017

作者： Snodgrass, Sam Ontañón, Santiago Drexel University 3141 Chestnut St PhiladelphiaPA19104 United States

ISBN: (纸本)9781577357926

procedural content generation via machine learning (PCGML) has been growing in recent years. However, many PCGML approaches are only explored in the context of linear platforming games, and focused on modeling structural level information. Previously, we developed a multi-layer level representation, where each layer is designed to capture specific level information. In this paper, we apply our multilayer approach to Lode Runner, a game with non-linear paths and complex actions. We test our approach by generating levels for Lode Runner with a constrained multi-dimensional Markov chain (MdMC) approach that ensures playability and a standard MdMC sampling approach. We compare the levels sampled when using multi-layer representation against those sampled using the single-layer representation;we compare using both the constrained sampling algorithm and the standard sampling algorithm. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Markov processes

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Mixed reality meets procedural content generation in video games 12

Mixed reality meets procedural content generation in video g...

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12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016

作者： Azad, Sasha Saldanha, Carl Gan, Cheng Hann Riedl, Mark O. Georgia Institute of Technology School of Interactive Computing United States

ISBN: (纸本)9781577357735

The use of artificial intelligence and procedural content generation algorithms in mixed reality games is an unexplored space. We posit that these algorithms can enhance the gameplay experience in mixed reality games. We present two prototype games that use procedural content generation to design levels that make use of the affordances in the players physical environment. The levels produced can be tailored to a user, customizing gameplay difficulty and affecting how the player moves around the real-world environment. ©2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mixed reality

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