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.

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 Workshops Held at the Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report

AI Magazine ◽  
2014 ◽  
Vol 35 (2) ◽  
pp. 65-68
Author(s):  
Antonios Liapis ◽  
Michael Cook ◽  
Adam M. Smith ◽  
Gillian Smith ◽  
Alexander Zook ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Design Process ◽  
Game Design ◽  
International Workshop ◽  
Conference Program ◽  
Digital Entertainment ◽  
Second International

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. Workshops were held on the two days prior to the start of the main conference, giving attendees a chance to hold in-depth discussions on topics that complement the themes of the main conference program. This year the workshops included the First Workshop on AI and Game Aesthetics (1 day), The Second Workshop on AI in the Game Design Process (1 day), The Second International Workshop on Musical Metacreation (2 day), The Sixth Workshop on Intelligent Narrative Technologies (2 day).

A Short Introduction to Procedural Content Generation Algorithms for Videogames

2019 ◽  
Vol 28 (02) ◽  
pp. 1930001 ◽  
Author(s):  
Nicolas A. Barriga
Keyword(s):  
Machine Learning ◽  
Short Introduction ◽  
Open Problems ◽  
Content Creation ◽  
Procedural Content Generation ◽  
Different Types ◽  
Videogame Industry ◽  
Potential Impact ◽  
Content Generation

One of the main costs of developing a videogame is content creation. Procedural Content Generation (PCG) can help alleviate that cost by algorithmically generating some of the content a human would normally produce. We first describe and classify the different types of content that can be automatically generated for a videogame. Then, we review the most prominent PCG algorithms, focusing on current research on search-based and machine learning based methods. Finally, we close with our take on the most important open problems and the potential impact solving them will have on the videogame industry.

scholarly journals Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

2020 ◽  
Author(s):  
Vanessa Volz ◽  
Niels Justesen ◽  
Sam Snodgrass ◽  
Sahar Asadi ◽  
Sami Purmonen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Procedural Content Generation ◽  
Global Patterns ◽  
Content Generation

scholarly journals Procedural Content Generation via Machine Learning (PCGML)

2018 ◽  
Vol 10 (3) ◽  
pp. 257-270 ◽  
Author(s):  
Adam Summerville ◽  
Sam Snodgrass ◽  
Matthew Guzdial ◽  
Christoffer Holmgard ◽  
Amy K. Hoover ◽  
...  
Keyword(s):  
Machine Learning ◽  
Procedural Content Generation ◽  
Content Generation

Reports on the Fourth Artificial Intelligence for Interactive Digital Entertainment Conference Workshops

AI Magazine ◽  
2012 ◽  
Vol 33 (1) ◽  
pp. 55-56
Author(s):  
David Elson ◽  
Jonathan Rowe ◽  
Adam M. Smith ◽  
Gillian Smith ◽  
Emmett Tomai
Keyword(s):  
Artificial Intelligence ◽  
Design Process ◽  
Game Design ◽  
Stanford University ◽  
Digital Entertainment

The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE-11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one-day workshops were held on October 11: Artificial Intelligence in the Game Design Process, and Intelligent Narrative Technologies. The highlights of each workshop are presented in this report.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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