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.

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).

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.

A data-driven procedural-content-generation approach for educational games

2018 ◽  
Vol 34 (6) ◽  
pp. 731-739 ◽  
Author(s):  
D. Hooshyar ◽  
M. Yousefi ◽  
M. Wang ◽  
H. Lim
Keyword(s):  
Educational Games ◽  
Data Driven ◽  
Procedural Content Generation ◽  
Content Generation

scholarly journals Playing an Educational Game Featuring Procedural Content Generation: Which Attributes Impact Players’ Curiosity?

RENOTE ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 254-263
Author(s):  
Luiz Rodrigues ◽  
Jacques Brancher
Keyword(s):  
Empirical Study ◽  
Educational Games ◽  
Educational Game ◽  
Procedural Content Generation ◽  
Player Experience ◽  
Content Generation

Understanding which and how attributes impact player experience can contribute to designing more tailored tools, providing concerns on how to improve these. However, there is a gap in the understanding of what impacts learners’ experience when interacting with Educational Games (EG) featuring Procedural Content generation (PCG) as these have been scantly used together. This article presents an empirical study on which attributes impact both math’s and game’s curiosity of players when interacting with an EG that uses PCG. The results show the attributes that led to higher or lower curiosity, as well aswhich of them are associated with it. Hence, advancing the understanding of what drives players’ curiosity, contributing to the design of EG that feature PCG.

A Procedural Content Generation-Based Framework for Educational Games: Toward a Tailored Data-Driven Game for Developing Early English Reading Skills

2017 ◽  
Vol 56 (2) ◽  
pp. 293-310 ◽  
Author(s):  
Danial Hooshyar ◽  
Moslem Yousefi ◽  
Heuiseok Lim
Keyword(s):  
Language Learning ◽  
Reading Skills ◽  
Educational Games ◽  
Research Problem ◽  
Educational Game ◽  
Learning Goals ◽  
Procedural Content Generation ◽  
English Reading ◽  
Early English ◽  
Content Generation

Automated content generation for educational games has become an emerging research problem, as manual authoring is often time consuming and costly. In this article, we present a procedural content generation framework that intends to produce educational game content from the viewpoint of both designer and user. This framework generates content by means of genetic algorithm, and thereby offers designers the ability to control the process of content generation for various learning goals according to their preferences. It further takes into consideration how the content can adapt according to the skill of the users. We demonstrate effectiveness of the framework by way of an empirical study of human players in an educational language learning game aiming at developing early English reading skills of young children. The results of our study confirm that users’ performance measurably improves when game contents are customized to their individual ability, in contrast to their improvement in uncustomized games. Moreover, the results show that the lowest proficiency participants demonstrated greater improvements in performance while playing the customized game than did the more highly proficient participants.

Socially Shared Regulation of Learning: A Review

2015 ◽  
Vol 20 (3) ◽  
pp. 190-203 ◽  
Author(s):  
Ernesto Panadero ◽  
Sanna Järvelä
Keyword(s):  
Empirical Evidence ◽  
Learning Theory ◽  
Qualitative Data ◽  
Social Regulation ◽  
Observation Data ◽  
Optimal Form ◽  
Self Regulated Learning ◽  
The Past ◽  
Recorded Observation ◽  
Socially Shared Regulation

Abstract. Socially shared regulation of learning (SSRL) has been recognized as a new and growing field in the framework of self-regulated learning theory in the past decade. In the present review, we examine the empirical evidence to support such a phenomenon. A total of 17 articles addressing SSRL were identified, 13 of which presented empirical evidence. Through a narrative review it could be concluded that there is enough data to maintain the existence of SSRL in comparison to other social regulation (e.g., co-regulation). It was found that most of the SSRL research has focused on characterizing phenomena through the use of mixed methods through qualitative data, mostly video-recorded observation data. Also, SSRL seems to contribute to students’ performance. Finally, the article discusses the need for the field to move forward, exploring the best conditions to promote SSRL, clarifying whether SSRL is always the optimal form of collaboration, and identifying more aspects of groups’ characteristics.

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

Calligraphy, Psychology and the Confucian Literati Personality

2021 ◽  
pp. 097133362199044
Author(s):  
Henry S. R. Kao ◽  
Min Xu ◽  
Tin Tin Kao
Keyword(s):  
Visual Attention ◽  
Personality Traits ◽  
Empirical Evidence ◽  
Behavioural Change ◽  
Strong Association ◽  
Psychological Theory ◽  
Confucian Culture ◽  
Chinese Calligraphy ◽  
Beneficial Effects ◽  
The Past

Our research in the past 40 years has identified beneficial effects of Chinese calligraphy handwriting (CCH) practice on visual attention, cognitive activation, physiological slowdown, emotional relaxation and behavioural change. We hypothesised that these outcomes may constitute a compressive set of foundations which could impact several traits of Chinese personality within the context of Confucian culture and values. Here, we give a brief overview of the background of CCH and its effect in the cognitive, physiological and bio-emotional domains. We then provide empirical evidence showing strong association of CCH and personality traits and discuss the results in the contexts of calligraphy practice and Confucian literati personality, Confucianism and Chinese personalities as well as calligraphy writing and tool-using psychological theory.

scholarly journals Through what mechanisms do protected areas affect environmental and social outcomes?

2015 ◽  
Vol 370 (1681) ◽  
pp. 20140267 ◽  
Author(s):  
Paul J. Ferraro ◽  
Merlin M. Hanauer
Keyword(s):  
Experimental Data ◽  
Protected Areas ◽  
Empirical Evidence ◽  
Strong Evidence ◽  
Protected Area ◽  
Evidence Base ◽  
Social Outcomes ◽  
Causal Effects ◽  
The Past ◽  
Made In

To develop effective protected area policies, scholars and practitioners must better understand the mechanisms through which protected areas affect social and environmental outcomes. With strong evidence about mechanisms, the key elements of success can be strengthened, and the key elements of failure can be eliminated or repaired. Unfortunately, empirical evidence about these mechanisms is limited, and little guidance for quantifying them exists. This essay assesses what mechanisms have been hypothesized, what empirical evidence exists for their relative contributions and what advances have been made in the past decade for estimating mechanism causal effects from non-experimental data. The essay concludes with a proposed agenda for building an evidence base about protected area mechanisms.

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