Summer School Description
For the fourth International Summer School on Artificial Intelligence and Games, we are returning to Chania, Greece! Join us between August 29 and September 2, 2022!
The summer school is dedicated to the uses of artificial intelligence (AI) techniques in and for games. After introductory lectures that explain the background and key techniques in AI and games, the school will introduce participants the uses of AI for playing games, for generating content for games, and for modeling players.
This school is suitable for industrial game developers, designers, programmers and practitioners, but also for graduate students in games, artificial intelligence, design, human-computer interaction, and computational intelligence.
The main lecturers are Georgios N. Yannakakis and Julian Togelius, co-authors of the AI and Games textbook (http://www.gameaibook.org), the first comprehensive textbook on the use of AI in games. During the first phase of the school theoretical lectures will be complemented by guest lectures on special topics in game AI and by hands-on workshops given by world-leading practitioners. For the second phase of the school, we plan a game AI jam on the taught material.
Previous Summer Schools
The 4th International Summer School on AI and Games is organised by modl.ai.
Modl.ai creates unique AI solutions that empower game developers around the world by automating game development and enhancing player engagement by embedding AI technology in key development stages.
Georgios N. Yannakakis
Georgios N. Yannakakis (yannakakis.net) is a Co-Founder and Research Director (Malta) of modl.ai, and Professor and Director of the Institute of Digital Games, University of Malta. He is a leading expert of the game artificial intelligence research field with core theoretical contributions in machine learning, evolutionary computation, affective computing and player modelling, computational creativity and procedural content generation. He has published more than 300 papers and his work has been cited broadly. He has attracted funding from several EU and national research agencies and received multiple awards for published work in top-tier journals and conferences. His work has been featured in New Scientist, Science Magazine, The Guardian, Le Monde and other venues. He is regularly invited to give keynote talks in the most recognised conferences in his areas of research activity and has organised a few of the most respected conferences in the areas of game AI and game research. He is currently the Editor in Chief of the IEEE Transactions in Games. He has been an Associate Editor of the IEEE Transactions on Computational Intelligence and AI in Games and the IEEE Transactions on Affective Computing journals. He is the co-author of the Artificial Intelligence and Games Textbook.
Julian Togelius (julian.togelius.com) is a Co-Founder and Research Director (New York) of modl.ai, and an Associate Professor at the Department of Computer Science and Engineering at the New York University Tandon School of Engineering. Previously, he was an Associate Professor at the Center for Computer Games Research, IT University of Copenhagen and among the founders of the procedural content generation research field. Togelius has introduced core procedural generation paradigms and frameworks for game content such as the Experience-driven Procedural Content Generation (EDPCG) framework and the Search-based Procedural Content Generation (SBPCG) paradigm which define two of the leading research trends within procedural content generation. EDPCG couples player experience modelling and procedural content generation so that game content is generated in a personalised manner for affecting the experience of the player and SBPCG offers a taxonomy for the generation of game content through search. He co-edited the first book on Procedural Content Generation in Games. Togelius' research has appeared in respected international media such as New Scientist, and Le Monde. He is the co-author of the Artificial Intelligence and Games textbook.
Introduction to the Summer School and Game AI
This session is dedicated to introducing the format of the summer school and explaining how artificial intelligence techniques can be used in and for games. After an introductory part that will focus on the history, background and key techniques used in AI and games, we will outline how to best use AI algorithms to play games, to generate content for games and to model players.
Search-Based and Constructive Procedural Content Generation
Machine Learning-Based, Mixed-Initiative and Experience-Driven Procedural Content Generation
Once we have obtained reliable models of players the next obvious question is how we can possibly design appropriate games for them. Games that have both the necessary aesthetic elements and functional properties for their designers and players. Methods derived from procedural content generation can be coupled with player models to yield entirely novel and personalised content for each player or designer. With such technology we can debug the experience attributed to each content type we design in an autonomous or a designer-assisted way.
How can we possibly detect behavioral patters, experiences elicited and decision made by players in a reliable manner? In this talk we will be taking you through the full cycle of the game affective loop with a focus on game experience elicitation, experience annotation and machine learning for the creation of models of players. The player modeling technology we will introduce is directly applicable for modeling both behavioral (player analytics) and experience aspects of play.
AI for Playing Games
Founder & CEO, askblu.ai
Entrepreneur (founder of OpeMind Intl in 1991, sold to Mindscape in 2006) and software Development wizard, Dominique has been for 3 years the CTO of Mindscape France. Dominique founded Happy Blue Fish in July 2009 to embrace the mobile gaming revolution. In 2017, he launched the DeepTech SaaS project askblu.ai, so every game studio and publisher can benefit from AI and Data Science with no upfront cost.
Data Scientist, askblu.ai
Real-Time Data Science for Mobile Games
What is Real-Time Data Science for mobile games? How does it work? What are the key challenges? The talk is focusing on Real-Time Churn Prediction.
Founder, Kate Compton consulting
Dr Kate Compton (@galaxykate) is a long-time generative artist, inventor, programmer, and Assistant Professor of Instruction at Northwestern University. She wrote the first paper on procedural platform-game levels, generated the planets for the video game Spore, created the language Tracery which runs over 10000 community-made bots on Twitter, and invented an early phone-based AR system. Her mission is to design artificial intelligence to augment human creativity, and to create tools that bring AI into the hands of poets, artists, kids, and weirdos.
All Together Now: Technology Design for Social Creativity
The pandemic has been a year of separation and isolation, but it's also a time when we've never been more technologically connected. Let's explore the ways that we are using new tools (and old ones in new ways) to come together in creative play. Discover a world of AI gameshow hosts, VR Shakespeare, Zoom roleplaying games, Animal Crossing birthdays, and Google Spreadsheet parties, and find out why they all work, and what roles AI can play in being a better host for our parties.
Principal Researcher, Microsoft Research
Sam is a Principal Researcher in the Deep Reinforcement Learning for Games group at Microsoft Research Cambridge. He received his PhD on multi-agent reinforcement learning in 2013 from the University of York; was a postdoc from 2013 to 2015, working on game analytics; and then was on the faculty from 2016 until joining Microsoft in 2018. Devlin has published more than 50 papers on reinforcement learning and game AI in leading academic venues and presents regularly at games industry events including Develop and the Game Developers Conference (GDC).
Sr. Research Engineer, SEED - Electronic Arts
Linus Gisslén is a researcher and team lead at SEED of a team looking into how to apply state-of-the-art machine learning techniques to games and game testing. His research topics involve the application of Reinforcement Learning but also Supervised Learning in games, Procedural Content Generation, and game playtesting. Before joining SEED and EA he was a researcher in various groups and has done a postdoc in ML. SEED is an advanced R&D group at Electronic Arts whose purpose is to push the boundaries of game experiences and to drive innovation in how games are created and played in the future.
Research Scientist, FAIR - Meta AI
Research Scientist, Google
Lead Data Scientist, Massive Entertainment
In the gaming industry, he is leading a data science team that supports game productions with advanced data processing solutions, pursuing the goal of enhancing player experience. He constantly aims at pushing boundaries of what data science brings to the complex art of crafting AAA video games, and published in recognized conferences such as IEEE Conference on Games or IEEE Big Data Conference. He is presently working on several projects related to main franchises of the studio, namely Tom Clancy's The Division 2, Avatar, and Star Wars.
Player Builds Segmentation from In-Game Telemetry: The BaT Clustering Approach
Most video games with high end-game replayability and long life span receive regularly new content and updates to keep their player base engaged. It can be for example through new seasonal challenges, ingame items, skins, missions or game modes. For games having RPG mechanics that open wide possibilities in terms of character customization (such as equipped items or unlocked skills), players usually create and refine "builds" that match their preferred playstyles. Understanding the builds effectively used by players in-game, before and after a title update release, is key for designers and other stakeholders to assess the reception of the newly introduced game elements.
We found that applying traditional unsupervised machine learning approaches often fail at extracting meaningful and actionable results. This is notably due to the combinatorial explosion inherent to the nature of the player data in this case, but also to the nature of this data. We experimented and developed a novel, original method - called BaT Clustering - to automatically segment big datasets of in-game player data into coherent "player builds" from a game perspective. The method leverages unsupervised machine learning together with text-based encoding based on game knowledge, and provides results in a fast, efficient, and scalable way. Most importantly, the produced output is easily interpretable by a non-data-specialist audience, that can effectively leverage its insights in the game production cycle.
We show examples of results found when applying this method to real player data from the game Tom Clancy's The Division 2.
AI Engineer, Sony AI
Outracing champion Gran Turismo drivers with deep reinforcement learning
Gran Turismo Sophy is a revolutionary superhuman racing agent designed to compete against top Gran Turismo® Sport drivers and elevate their gaming experience.
GT Sophy was trained using novel deep reinforcement learning techniques, including state-of-the-art learning algorithms and training scenarios developed by Sony AI, using Gran Turismo Sport, a real driving simulator, and by leveraging Sony Interactive Entertainment's cloud gaming infrastructure for massive scale training.
Game AI Jam Facilitator and Guest Lecturer
Lecturer at the Institute of Digital Games, University of Malta.
Antonios Liapis is a Lecturer at the Institute of Digital Games, University of Malta, where he bridges the gap between game technology and game design in courses focusing on human-computer creativity, digital prototyping and game development. His research focuses on Artificial Intelligence as an autonomous creator or as a facilitator of human creativity. His work includes computationally intelligent tools for game design, as well as computational creators that blend semantics, visuals, sound, plot and level structure to create horror games, adventure games and more. He has also co-organized numerous game jams, and has participated in even more!
Game AI Jam
During the last two afternoons of the Summer School, we will participate in a game AI jam, facilitated by Antonios Liapis. During the jam students will work in teams, focusing on creating a game environment for applying or testing the algorithms discussed during the remainder of the school. Alternatively, teams can also create a tool rather than a full game, such as a generator for game content (levels, graphics, audio...). The two-day jam will conclude with a "demo hour" where all students and lecturers can see and play with the different projects, and talk to each other about best practices and lessons learned.
Webmaster and Publicity Chair
AI Researcher at modl.ai
David Melhart is an AI Researcher for Modl.ai, specializing in User Research and Player Modelling. He was the Communication Chair of FDG 2020 and has been a recurring organizer and Publicity Chair of the Summer School series on Artificial Intelligence and Games (2018-2022). With more than a dozen publications, an industry collaboration with Ubisoft, and multiple conference appearances behind his back, he is an experienced presenter. David earned his Ph.D. in AI and Games Research at the Institute of Digital Games, University of Malta in 2021. He received a master's degree in Cognition and Communication from the University of Copenhagen in 2016. His academic research focuses on Machine Learning and Affective Computing.
Summer School Program
Timeslots in the program correspond to the Eastern European Summer Time (GMT+3) timezone.
|09:00-09:30|| Welcome Session and Game Jam|
Facilitator: Antonios Liapis Introduction
Georgios N. Yannakakis & Julian Togelius, Antonios Liapis
|09:30-10:30||AI and Games: Introduction|
Georgios N. Yannakakis & Julian Togelius
|10:30-11:30||AI that Plays|
|15:30-16:30||Outracing champion Gran Turismo drivers with deep reinforcement learning|
|09:30-10:30||AI that Designs|
|10:30-11:30||All Together Now: Technology Design for Social Creativity|
|15:30-16:30||Frontiers in PCG|
Julian Togelius & Georgios Yannakakis
|09:30-10:30||AI that Experiences|
|10:30-11:30||Real-Time Data Science for Mobile Games|
Dominique Busso & Bassel Masri
|12:00-13:00||Player Builds Segmentation from In-Game Telemetry: The BaT Clustering Approach|
|15:30-16:30||Frontiers in Player Modeling|
Facilitator: Antonios Liapis
Facilitator: Antonios Liapis
Facilitator: Antonios Liapis
Facilitator: Antonios Liapis
Facilitator: Antonios Liapis
|from 17:30||Farewell Event|
Expectations on Participants
While the Summer School on Artificial Intelligence and Games is open to participants at varying levels of expertise and seniority, you will get more out of your participation in the summer school if you come equipped with some conceptual and technical knowledge. In particular, the following topics are worth touching up on, or reading up on if you do not already know them:
Tree search algorithms: informed and uninformed search (depth-first, breadth-first, A*); game tree search (Minimax); Monte Carlo Tree Search.
Machine learning: basic concepts (supervised, unsupervised, reinforcement learning); neural networks; decision trees.
If you are unsure about your level of understanding of artificial intelligence and machine learning, try reading Chapter 2 ("AI Methods") of the Artificial Intelligence and Games book, which covers these topics. You will find pointers there to material that can help you refresh your knowledge of particular topics.
Programming: it greatly helps to be able to program in some language. Which particular language is of lesser importance. Wherever possible, examples will be given in pseudocode so as to facilitate understanding across language barriers. However, some examples may be given in e.g. Python, Java or C#. The various tutorials and hands-on workshops are expected to use different frameworks and languages. We will add the list of specific language requirements per tutorial the closer we approach the school.
Game engines: knowledge of a game engine such as Unity will be useful during the concluding game AI jam.
Bringing your own laptop is similarly beneficial for participating in the practical sessions. We will not be able to provide a laptop for you during the summer school.
Apart from this, we only need you to come equipped with an open mind and a willingness to learn.
We want the First Summer School on Artificial Intelligence and Games to be joyful as well as useful occasion for all of us. Remember that participants come from many different countries, backgrounds, and experience, and treat everyone with respect and kindness. Please talk to the organisers if we can do something to improve your experience.
Software Guidelines for Participants
The various hands-on tutorials require different software installed on your laptops. To make the most of the tutorials please have the following software installed and prepared.
More details will be announced before the summer school.
Only limited number of in-person seats are available.
The in-person registration includes coffee breaks, lunches for 5 days,
and 2 reception dinners (welcome and farewell) as well.
Regular Registration Prices until August 1!
After August 1 late fees apply!
- Access to Discord
- Access to Online Course Material
- Lectures Streamed Online
- Access to Discord
- Access to Online Course Material
- Live Lectures & Workshops
- One-on-One Speed Meetings with Lecturers
- Networking with Guest Lecturers
- Game AI Jam
- Lunches at the Minoa Palace Resort Hotel
- Two Dinner Events
- Social Events
Stay in contact for future updates!Subscribe to our Mailing list
Facing problems with the registration?gro.koobiaemag@loohcs
Don't hesitate to contact us!
Our partners at Sony AI offer 8 full scholarship seats for students at the International Summer School on AI and Games!
We offer this opportunity to students based on an outstanding CV and a short, one-paragraph letter of motivation emphasizing the role of this summer school in the development of your current or future game AI projects or research. Selected candidates will have their registration fee waived.
The applications should be submitted to firstname.lastname@example.org with the subject line: "Scholarship 2022".
The deadline for applications is May 31.
Students from developing countries will be prioritised (based on the OECD/DAC List of ODA Recipients).
The Summer School venue will be held at the Minoa Palace Resort Hotel.
Minoa Palace Resort Hotel in Chania was built in 2002 and has been established as one of the leading 5-star resorts in Chania, Crete. It stands out due its aura of tranquility, harmoniously blended in with the ocean breeze and the lush green surroundings. A preferable choice for artistic rendez-vous, the resort is an equally great host for all sorts of corporate events, conferences, meetings, parties and wedding ceremonies.
The Minoa Palace Resort Hotel reserves rooms at the venue for the Summer School participants. If you wish to stay at the venue take a look at the booking site for more information.
You will be able to proceed with their booking until 01/06/2022. After this date, the room block will be removed and availability will be upon request. In case you have special requests, please send an email to the reservation department of Minoa Palace Resort Hotel at email@example.com