Island Generator pada Game Open world Menggunakan Algoritma Perlin noise

Repositor ◽  
2020 ◽  
Vol 2 (7) ◽  
pp. 965
Author(s):  
Naufal Azzmi ◽  
Lailatul Husniah ◽  
Ali Sofyan Kholimi
Keyword(s):  
Performance Testing ◽  
Game Development ◽  
Procedural Content Generation ◽  
Open World ◽  
Game World ◽  
Perlin Noise ◽  
And Performance ◽  
Game Developers ◽  
Pcg Method ◽  
Content Generation

AbstrakPerkembangan game pada saat ini berkembang dengan sangat cepat, dalam perkermbangan game topik AI adalah topik yang paling banyak diteliti oleh beberapa peneliti khususnya pada pembuatan suatu konten game menggunakan metode PCG (procedural content generation). Pada pembuatan sebuah game world menggunakan metode PCG sudah banyak developer game yang sukses dengan mengimplementasikan metode ini, metode ini banyak digunkan pada geme dengan genre RPG, Rouglikes, Platformer, SandBox, Simulation dan lain sebagainya, Pada penelitian ini berfokus pada pengembangan sebuah game world generator untuk game berjenis open world yang berupa sebuah kepulauan dengan metode PCG dengan menggunakan algoritma perlin noise sebagai algoritma pembentuk textur utama pulau yang dimana pada penelitian ini memanfaatkan beberapa variable noise seperti octave, presistance dan lacunarity guna untuk menambah kontrol dari hasil textur yang dihasilkan serta algoritma penempatan pulau untuk membuat sebuah game world yang menyerupai sebuah kepulauan. Dari hasil uji generator terkait degan pengujian playability dan performa dapat disimpulkan bahwa generator yang dikembangkan playable serta performa yang dianaliasa menggunakan notasi Big O menunjukkan  (linear). Abstract Game development is currently growing very fast, game development AI is the most discussed topic by most researchers especially in the developing of game content using the PCG (procedural content generation) method. In making a game world using the PCG method, many game developers have succeeded by implementing this method, this method is widely used on RPGs, Rouglikes, Platformers, SandBox, Simulations and ect,. This study focuses on developing a game world generator game for open world type games in the form of an archipelago using the PCG method using the noise perlin algorithm as the island's main texturizing algorithm which in this study utilizes several noise variables such as octave, presistance and use for add control of the texture results as well as the island placement algorithm’s to create a game world that resembles an archipelago form. From the generator test results related to the playability and performance testing, it shows that map are being generated by the generators are playable and performance that are analyzed using Big O notation show O (n) (linear).

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report

AI Magazine ◽  
2014 ◽  
Vol 35 (2) ◽  
pp. 61-64
Author(s):  
Gita Sukthankar ◽  
Ian Horswill
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Design ◽  
Interactive Narrative ◽  
Procedural Content Generation ◽  
Digital Entertainment ◽  
Interactive Entertainment ◽  
Game Developers ◽  
Autonomous Characters ◽  
Content Generation

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. The mission of the AIIDE conference is to provide a forum for researchers and game developers to discuss ways that AI can enhance games and other forms of interactive entertainment. In addition to presentations on adapting standard AI techniques such as search, planning and machine learning for use within games, key topic areas include creating realistic autonomous characters, interactive narrative, procedural content generation, and integrating AI into game design and production tools.

scholarly journals A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation

2019 ◽  
Vol 4 (3) ◽  
pp. 308
Author(s):  
Muhammad Hafis ◽  
Herman Tolle ◽  
Ahmad Afif Supianto
Keyword(s):  
Empirical Evidence ◽  
Educational Games ◽  
Generation Process ◽  
Game Based Learning ◽  
Procedural Content Generation ◽  
The Past ◽  
Human Effort ◽  
Pcg Method ◽  
Multiple Type ◽  
Content Generation

Procedural Content Generation (PCG) is an emerging field of study in computer science that focuses on automating the process of generating content by using algorithm, making the content generation process with less human effort. However, a more specific empirical evidence on how it is being used in a game-related implementation are still lacking. This paper presents the findings of review performed in the past 5 years looking on how PCG are being applied in game-related content, whether it is from the basic paper characteristic to analyze the trends, the field of PCG itself, and the game domain of game model and game genre. The studies had shown that PCG are being used extensively in game-related content but has seen more uses on specific type of contents rather than being used ubiquitously in all sorts of contents. The result shown that there are not specific best type of PCG method or algorithm being used instead an array of approach can be used based on what content being created. Result also shown that PCG are being used in multiple type of games, but similarly, based on the paper found, only certain types of game benefits PCG extensively such as action and platforming games while other model and genre of games have not seen much PCG application yet. Further studies are also required to analyze how experimentation and evaluation of PCG are being done as well as PCG domain in educational games as well as game-based learning, the quality catachrestic being analyzed on the papers are also worth mentioning to understand the underlying result of PCG usage in game-related contents.

scholarly journals Procedural Dungeon Generation: A Survey

2021 ◽  
Vol 12 (1) ◽  
pp. 83-101
Author(s):  
Breno M. F. Viana ◽  
Selan R. Dos Santos
Keyword(s):  
Level Structure ◽  
Game Development ◽  
Content Creation ◽  
Procedural Content Generation ◽  
Current State ◽  
Game Features ◽  
Mixed Initiative ◽  
Input Level ◽  
Content Generation

Procedural content generation (PCG) is a method of content creation entirely or partially done by computers. PCG is popularly employed in game development to produce game content, such as maps and levels. Representative examples of games using PCG are Rogue (1998), which introduced the rogue­like genre, and No Man’s Sky (2016), which generated whole worlds with fauna and flora. PCG may generate final contents, ready to be added to a game, or intermediate contents, which might be polished by human designers or work as an input level sketch to be interpreted by a level translator. In this paper, we survey the current state of procedural dungeon generation (PDG) research, a PCG subarea, applied in the context of games. For each work we selected in this survey, we examined and compared how they created game features, what type of level structure and representation they propose, which content generation strategy they applied, and, finally, we classify them according to the taxonomy of procedural content generation proposed by Togelius et al. (2016). The most relevant findings of our survey are: (1) PDG for 3D levels has been little explored; (2) few works supported levels with barriers, a game mechanic which temporarily blocks the player progression, and; (3) mixed-initiative approaches, i.e., software that helps human designers by making suggestions to the levels being created, are little explored.

Reinforcement Learning Content Generation for Virtual Reality Applications

2019 ◽  
Author(s):  
Christian E. Lopez ◽  
Omar Ashour ◽  
Conrad S. Tucker
Keyword(s):  
Virtual Reality ◽  
Reinforcement Learning ◽  
Design Space ◽  
Generative Models ◽  
Primary Objective ◽  
Large Datasets ◽  
Procedural Content Generation ◽  
Learning Content ◽  
Pcg Method ◽  
Content Generation

Abstract This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate new content (e.g., environments, levels) in order to improve user experience. Researchers have started exploring the integration of Machine Learning (ML) algorithms into their PCG methods. These ML approaches help explore the design space and generate new content more efficiently. The capability to provide users with new content has great potential for learning applications. However, these ML algorithms require large datasets to train their generative models. In contrast, RL based methods take advantage of simulation to train their models. Moreover, even though VR has become an emerging technology to engage users, there have been few studies that explore PCG for learning purposes and fewer in the context of VR. Considering these limitations, this work presents a method that generates new VR environments by training an RL agent using a simulation platform. This PCG method has the potential to maintain users’ engagement over time by presenting them with new environments in VR learning applications.

scholarly journals Algoritma Maze Generator Recursive Backtracking Untuk Membuat Prosedural Labirin Pada Game Petualangan Labirin 3D

Repositor ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 373
Author(s):  
Elbert Setiadharma ◽  
Lailatul Husniah ◽  
Ali Sofyan Kholimi
Keyword(s):  
Video Games ◽  
Video Game ◽  
System Behavior ◽  
Game Development ◽  
Important Thing ◽  
Procedural Content Generation ◽  
Time Solution ◽  
Short Period ◽  
Content Generation

Abstrakperkembangan game diranah industri berkembang pesat, banyak inovasi yang telah diciptakan agar dapat memaksimalkan kinerja sistem yang mulai kian beragam bersamaan dengan banyak hal yang ditawarkan. melihat perkembangan ini banyak developer pintar memilah metode-metode yang diperlukan agar proses pembuatan game tidak terhambat. salah satu komponen dalam game yang perlu diperhatikan lebih adalah environment, dimana environment merupakan unsur game yang sangat penting. kebanyakan developer merasa kesulitan dalam menciptakan environment yang beragam namun dalam waktu yang singkat. salah satu cara menyiasati adalah dengan cara menerapkan metode procedural content Generator. konteks environment yang mudah dalam penerapan untuk skala prototyping adalah labirin, dimana segala unsur komponen game didalamnya dapat ditentukan sesuai variabel yang telah dirancang oleh developer game. metode algoritma penerapan untuk PCG environment beragam terutama pada bidang labirin, salah satunya algoritmanya adalah Recursive Backtracking, dimana algoritma ini banyak digunakan demi mendapatkan environment yang sesuai dengan kehendak developer game serta banyak memiliki keuntungan dibandingkan dengan algoritma PCG dalam bidang labirin lainnya. berbekal penelitian sebelumnya yang mengatakan bahwa algoritma Recursive Backtracking merupakan algoritma yang tepat untuk mengolah konten labirin secara prosedural, maka penelitian ini akan dilakukan untuk membuktikan apakah algoritma Backtracking dalam pembuatan PCG untuk bidang labirin telah sesuai dengan pernyataan penelitian sebelumnya. Abstract Video game on industries scale were growing fast, many innovation behind that been created to get optimized system behavior. Watch this phenomenon, many game developer try get the finest method to achieve the best way on game development. One of the important thing that must to be cared on game development was environment. Many game developer getting trouble to made environment that had many variety but could be developed on short period of time. Solution for this problem is implement Procedural Content Generation method. Maze was one of the option that most easiest way to approach prototyping on game scale, where most of every Component video games could be placed depend on variable that developer design before. Algorithm for PCG environment had much kind, Recursive Backtracking is one of it. This Algorithm been used and much proven for its behavior for getting good result, moreover on Maze context. as its said on previous research, this Algorithm  was the best option to be implemented to create Maze on procuderal way. This research been intend to prove is it Recursive Backtrack could be the best way to implement as method to getting good result moreover for Maze.  

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

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