The Tutorials that are being held at the IEEE CoG 2019 are the following:
Details about each of the Tutorials can be found below:
As we develop our systems for generation and to play games, we want to have a clear understanding of the power of the method and the human cooperativeness in order to demonstratively prove an AI is meeting with its requirements. Further, games are artistic objects with aesthetic concerns which are best evaluated by human subjects testing. These human trials are is not usually performed by computer scientists.
In this respect, while focusing on correcting the technical dimensions of the system, it is easy to overlook player dimensions that are the major stakeholder of the product. Player dimension broadly constitutes their ability to feel that they are a part of the system and can easily immerse in the play. Though this does not mean, removing complexities and unpredictability that are sometimes crucial for an adventurous play journey. This means to remove confusions that are unnecessary and that design is taking into account all; visceral, behavioural and reflective levels of processing of player’s mind. This means considering all effects such as calmness, anxiety, hope, fear and expectations from player’s side.
The tutorial will examine the process of ethical approvals, participant selection, setup of the space and equipment, surveys of participants, data collection methods, data storage, and analysis. The tutorial is broadly on user experience investigation and a focus will be made as well on tools for the understanding of aesthetics and other similarly subjectively defined generation criteria used in many PCG and games objects.
(S'09--M'14; email@example.com) was born in Niagara-on-the-Lake, ON, Canada, on July 6, 1985. He received the B.Sc. (Hons.) with first-class standing in computer science with a concentration in software engineering, and M.Sc. in computer science from Brock University, St. Catharines, ON, Canada in 2007 and 2009, respectively. He received the Ph.D. in computer science from the University of Guelph in 2014. He previously worked for Magna International Inc. as a Manufacturing Systems Analyst and as a visiting researcher at ITU Copenhagen. He is currently an Assistant Professor and head of the Artificial Intelligence in Games Development Lab at Innopolis University in Innopolis, Republic of Tatarstan, Russia and an Adjunct Professor of Computer Science at Brock University, St. Catharines, ON, Canada.
(firstname.lastname@example.org) received the B.Sc. (Hons. with first-class standing) in Computer Engineering from BZU, Multan and the M.Sc. in Computer Engineering from UET, Lahore, Pakistan. Previously she was a visiting lecturer at the Institute of Business Management, University of Engineering and Technology Lahore (UET), Pakistan. Besides, Hamna Aslam worked in the energy sector of the government of Pakistan in the capacity of Software Developer. Presently she is a Ph.D. student as well as an instructor at Innopolis University since December 2015. Her research interests include Human Factors in Gaming, Intuitive Game Design and User testing.
Procedural audio is the generation of sounds from algorithms rather than samples. In a game context, procedural audio is a form of sound synthesis where sounds are created in real-time according to a set of programmatic rules and live input. This allows the procedural sounds to have infinite variety and far less storage requirements, and to depend directly on the game state. The Web Audio API allows procedural sound to be generated directly in the browser, making it an excellent tool for demonstrating procedural sound development, as well as for deployment in many online games. This tutorial will take the audience through the concepts behind procedural audio and the basics of the Web Audio API (and other relevant audio development environments), and the state of the art for research in this field. It will include live, practical coding of a procedural audio system, with audio demonstrations. This tutorial relates to the online procedural sound effects generation at https://fxive.com/index.html. It’s not dependent on that, but the website is a good demonstrator of what the tutorial covers.
Josh Reiss is a Professor with the Centre for Digital Music at Queen Mary University of London, where he teaches Digital Audio Effects and Sound Design, and leads the audio engineering research team. He has published over 200 scientific papers, including five best paper or demonstrator awards, and co-authored the textbook Audio Effects: Theory, Implementation and Application. He is a former governor of the Audio Engineering Society and chair of their Publications Policy Committee. He co-founded the spin-out company LandR, providing intelligent music mastering, and the spin-out FXive, delivering procedural sound design services
The GDMC Settlement Generation Challenge is about making an AI that can build an ‘’interesting’’ settlement for a given, unseen Minecraft map. In this tutorial we will cover the following topics:
1) What is the GDCM challenge and how can you participate?
2) Why is this an interesting computational creativity competition? What are the scientific challenges involved?
3) How do you get started with building your own AI? In this section we will introduce our two existing frameworks and walk through some examples. Ideally, this is organized as a possible code along
More information about the competition, its rules, previous entries, the existing frameworks and code, etc, can be found: http://gendesignmc.engineering.nyu.edu/
Search-based procedural content generation (PCG) is a very popular approach to generating various types of content for games, such as levels and weapons. It hinges on the specification of the following three components: a suitable representation for the content, which can then be searched by an algorithm guided by a fitness function. Most research focuses on one aspect of the problem definition and keeps the remaining components fixed. These parts of the problem are then usually treated as a black box.
However, taking a more holistic and application-agnostic approach to analysing search-based PCG instead has several potential benefits. A better understanding of the fitness landscape can allow to find good algorithms where all components work well together. In addition, knowledge about global optima can help assess the success of the PCG approach and whether even better content can be found.
We propose to use benchmarks for this kind of systematic analysis. In the tutorial, we demonstrate how the Game-Benchmark for Evolutionary Algorithms (GBEA) can be used for the purpose of gaining a detailed understanding of a given PCG problem. We further give examples of how this information can be used for improving the PCG algorithm.
Based on an example, we want to discuss the following topics in our talk in the order as indicated below:
1) Why analyse PCG problems? (Motivation as above)
2) Given we have a problem: how do we compile a benchmark?
3) What analysis is possible and which tools are provided in GBEA? (how to apply them)
4) How can the analysis results be used to improve PCG algorithms?
Link to the tutorial page: http://norvig.eecs.qmul.ac.uk/gbea/.
Boris Naujoks is a professor for Applied Mathematics at TH Köln - Cologne University of Applied Sciences (THK). He joint THK directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Meanwhile, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of THK. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.
Vanessa Volz is a post-doctoral research associate at Queen Mary University London, UK, with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK, in 2014. Her main research interests are two-fold: One is the analysis of game optimisation problems (such as search-based procedural content generation or automatic tuning / balancing) in terms of their complexity, uncertainty and other patterns. The other interest is the development of algorithms specifically suited for these problems, such as surrogate-assisted evolutionary algorithms that are able to handle non-symmetric noise and uncertainties.