Procedural Content Generation using Artificial Intelligence for Unique Virtual Reality Game Experiences

2019 ◽  
Author(s):  
Joao Pedro Assuncao Campos ◽  
Rafael Rieder
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
Procedural Content Generation ◽  
Content Generation

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.

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 Implementation Of L-System In Procedural City Generation Using Java

2010 ◽  
Vol 1 (2) ◽  
pp. 289
Author(s):  
Surya Sujarwo ◽  
William Salim ◽  
Ferry Yuwono
Keyword(s):  
Virtual Reality ◽  
Procedural Content Generation ◽  
Design And Implementation ◽  
Road Map ◽  
L System ◽  
Virtual City ◽  
The City ◽  
Content Generation

Article discusses about the design and implementation of procedural content generation using java, especially the generation of virtual city. It is applied by using L-System to generate the elements of the city and also using some images as the base models. This method is proven to be more effective because it can produce almost unlimited variations of city in short amount of time without any needs to modify the application. The result of this application is a road map which can be used in many areas such as virtual reality, games, or other related purposes.

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).

scholarly journals Deep learning for procedural content generation

2020 ◽  
Author(s):  
Jialin Liu ◽  
Sam Snodgrass ◽  
Ahmed Khalifa ◽  
Sebastian Risi ◽  
Georgios N. Yannakakis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Procedural Content Generation ◽  
Content Generation

scholarly journals Why a Virtual Assistant for Moral Enhancement When We Could have a Socrates?

2021 ◽  
Vol 27 (4) ◽  
Author(s):  
Francisco Lara
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
New Technologies ◽  
Personal Autonomy ◽  
Moral Enhancement ◽  
Moral Decisions ◽  
Educational Methodology ◽  
Better Than

AbstractCan Artificial Intelligence (AI) be more effective than human instruction for the moral enhancement of people? The author argues that it only would be if the use of this technology were aimed at increasing the individual's capacity to reflectively decide for themselves, rather than at directly influencing behaviour. To support this, it is shown how a disregard for personal autonomy, in particular, invalidates the main proposals for applying new technologies, both biomedical and AI-based, to moral enhancement. As an alternative to these proposals, this article proposes a virtual assistant that, through dialogue, neutrality and virtual reality technologies, can teach users to make better moral decisions on their own. The author concludes that, as long as certain precautions are taken in its design, such an assistant could do this better than a human instructor adopting the same educational methodology.

scholarly journals Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review

2021 ◽  
Vol 11 (7) ◽  
pp. 3253
Author(s):  
Umile Giuseppe Longo ◽  
Sergio De Salvatore ◽  
Vincenzo Candela ◽  
Giuliano Zollo ◽  
Giovanni Calabrese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Orthopaedic Surgery ◽  
Resident Training ◽  
Comprehensive Search ◽  
Virtual And Augmented Reality ◽  
Orthopaedic Training ◽  
Meta Analyses

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

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