After serious consideration, we regret to announce that due to the global pandemic caused by COVID-19 the 2020 Summer School is cancelled.
We return next year with the 3rd Summer School, which will be held in June 2021, in Copenhagen.

Summer School Description

The third International Summer School on Artificial Intelligence and Games will be held in Copenhagen, Denmark, from June 22 to 26, 2020. The school is organized by in partnership with Unity, DeepMind, and Creative Assembly (more partners will be announced soon).

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 (, 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

Main Organizer

The 3rd International Summer School on AI and Games is organised by 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.


More partners will be announced soon...
* The partnership is associated with Sahar Asadi's talk.

Supported by

The Royal Danish Academy of Fine Arts
Schools of Architecture, Design and Conservation



Georgios N. Yannakakis

Georgios N. Yannakakis ( is a Co-Founder and Research Director (Malta) of, 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 220 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 ScientistScience MagazineThe GuardianLe 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 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 currently an Associate Editor of the IEEE Transactions in Games. He is the co-author of the Artificial Intelligence and Games Textbook.


Julian Togelius

Julian Togelius ( is a Co-Founder and Research Director (New York) of, 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 Editor-in-Chief of the IEEE Transactions on Games (launch in January 2018) and 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.

Player Modelling

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


Guest Lecturers


Tom Schaul

Senior Research Scientist at Google DeepMind.

Tom Schaul is a Senior Research Scientist at Google DeepMind. His research includes on modular and hierarchical reinforcement learning, stochastic and black-box optimization with minimal hyperparameter tuning, and deep and recurrent neural networks with a special focus on games.

Tom holds a PhD from TU Munich (2011), which he completed under the supervision of Jürgen Schmidhuber at the Swiss AI Lab IDSIA. From 2011 to 2013 he was a postdoc at the Courant Institute of NYU, in the lab of Yann LeCun. He joined Google DeepMind first in 2013 as a senior researcher and later as a senior research scientist.

Deep RL and Games: AlphaStar Deep Dive


Deep RL and Games: The Key Concepts



Duygu Cakmak

Senior Programmer at Creative Assembly.

Duygu Cakmak is a Senior Programmer at Creative Assembly on the Total War campaign AI team. With a diverse background ranging from game AI development to software engineering, she joined Creative Assembly in 2015 first as an AI programmer, then later as a senior programmer. Duygu won the 2019 MCV Women In Games Awards for the Technical Impact of the Year.

AI Decision Making in Turn-Based Strategy Games

Turn-based strategy games present a very complex environment: a big map, a large number of entities, resources, and several gameplay systems. Players need to have an understanding of what each one of these systems do. They need to be able to make appropriate decisions based on many criteria such as what they want to achieve in the long term or how strong they are compared to their enemies. Similar to the player, every AI agent needs to make decisions on each of these systems. The decisions that the AI makes are highly dependent on the current state of the world, requiring a deep and careful analysis of its entities with limited memory and time budgets. In this lecture, we will talk about the challenges of developing AI for turn-based strategy games and how we designed and implemented our AI systems in the Total War series. The topics will include how we analyze the world, how we use Monte Carlo Tree Search in the resource allocation and army coordination systems, and how we developed a data-driven utility-based AI for the diplomacy system.


Sahar Asadi

AI Research Lead at King.

Sahar is a research lead at King where she drives AI research for the game. Sahar has obtained her Ph.D. on mobile robot olfaction from  Applied Autonomous Sensor System, Orebro University. Throughout her 8 year-long industry journey, she got to apply research to real problems in many different domains: user experience at Spotify, distributed deep learning at Clusterone, information retrieval and NLP at Meltwater, and product recognition at OculusAI.

AI for Playtesting in Games

Offering high-quality content and good user experience is essential in game development. Playtesting is a process to test game content. We apply AI to playtest games at King. The focus of this session will be on building AI solutions for automatic playtesting of content; the challenges, and existing solutions. This session is structured in two parts: in the first part, we discuss playtesting for games and present different AI methods applied for playtesting. As an example, we showcase some of our experience in playtesting at King. In the second part, we will try out a few of the discussed approaches.


Daniel McDuff

Principal Researcher at Microsoft.

Daniel is a Principal Researcher at Microsoft in the Human Understanding and Empathy group. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work on noncontact physiological measurement helped spawn a new field of imaging-based photoplethysmography. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR and the Center for Integrated Medicine and Innovative Technology (CIMIT). His projects have been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist, Scientific American and Forbes magazine. Daniel was named a 2015 WIRED Innovation Fellow, an ACM Future of Computing Academy member and has spoken at TEDx and SXSW.

AI, Human Understanding and Empathy in Gaming

Machine learning and AI present many opportunities for building new human-computer interfaces. This lecture will cover examples of state-of-the-art human sensing and synthesis algorithms that can be used to create computer and gaming systems that understand and adapt to users’ physiological states, expressions and style. It will address how these techniques can be used in end-to-end embodied conversational agents and what applications these might have in gaming. I will also suggest how giving agents their own emotional drives (such as fear or delight) could be used to create more intelligent artificial agents. Finally, these technologies present many opportunities; however, they also raise questions about the ethics of designing systems that measure and leverage highly personal data. The talk will contain proposals for design principals to help address these questions.


Vincent-Pierre Berges

Senior Machine Learning Engineer at Unity Technologies.

Vincent-Pierre is a Senior Machine Learning Engineer at Unity Technologies.  He was one of the founding team members of Unity Machine Learning Agents, an open-source toolkit that allows game developers to create behaviors in games using deep reinforcement learning.  The toolkit has become one of the top Github projects for deep learning and artificial intelligence. Vincent-Pierre holds Master's degrees from Stanford University and the École Polytechnique of France.

Using Unity and ML-Agents for Research, NPC AI, and More

This lecture and tutorial will walk through using Unity with the ML-Agents toolkit for conducting Machine Learning research, and in designing and testing games. We will walk through both the motivation and design of the toolkit, with a particular focus on scenarios applicable to game AI. The tutorial will also include an explanation of the kinds of algorithms being used to train machine learning agents, including various Reinforcement Learning and Imitation Learning methods. The first half will be a higher-level look, while the second half will take place in the context of a hands-on creation process, where we will walk through designing an environment, training an agent within it using ML-Agents, and finally deploying the trained model into a game which can be played.


Oskar Stålberg

Art, Design, "Programming", etc. at Plausible Concept.

Previously a Technical Artist at Ubisoft and Unity Developer at UsTwo. Oskar made a name for himself online with his procedural generation demos and is now bringing all that experience together into driving the development of Bad North.


To Be Announced...



Ahmad Azadvar

User Research Lead at Ubisoft Sweden & PhD candidate at Malmö University

From an early involvement with the entertainment industry to purely academic research, Ahmad has always been interested in what makes us engaged and transferred to a virtual world and even more importantly what keeps the engagement for longer periods. Working on over 30 video game projects including some of the most remarkable AAA titles under development and out in the market reaching to hundreds of millions of players, as well as sustained involvement with Academia in different ways, has been a great resource for him to maintain his enthusiasm, grow his knowledge and stay at the prime of Game User Research.

At Ubisoft Massive, Ahmad started his career mainly focusing on the Biometrics approach (psychophisiology) in user research. Currently, he oversee planning, development, set-up and analyzing various testing methods while enabling the crossing of a plethora of types of data collected within multiple disciplines & departments for an optimal simulation of the user experience. Ahmad also spearheaded the development of new qualitative and quantitative methodologies as well as data acquisition, analysis, visualization  and delivery approaches in order to contribute to the improved utilization of consumer research within the bigger team of Ubisoft.


Alessandro Canossa

Czar of Player Experience at

Dr. Alessandro Canossa has been straddling between the game industry and academia for many years. He has been Assistant Professor at the IT University of Copenhagen, Associate Professor at Northeastern University in Boston and he's now at the Royal Danish Academy of Fine Arts. In his research, he employs psychological theories of personality, perception, motivation and emotion to design games with the purpose of investigating individual differences in behavior among users of digital entertainment. His research focuses heavily on these topics: a) developing behavioral analysis methods that are able to account for granular spatial and temporal events, avoiding aggregation; b) designing and developing visual analytics tools that can enable any stakeholder to produce user driven content leveraging advanced statistics and machine learning.

He was also Senior User Researcher and Data Scientist at Massive Entertainment a Ubisoft studio, where he enjoyed tremendously investigating occult behavioral patterns and novel player modeling approaches while identifying the best processes for transferring knowledge from academic research to industry practices. He's now involved with Modl.AI, a company providing AI services to the game industry, where he's exploring how to triangulate data-driven insights with surveys and lab observations to advance the field of predictive analytics.

Predicting Players’ Motivations from Gameplay Data

In this workshop, based on the paper “Your gameplay says it all”, we will explore the process of predicting players’ motivations from their gameplay data. Player motivation is assessed with UPEQ a survey based on Self Determination Theory created by Ubisoft. Two datasets will be given to participants: a) responses to the questionnaire and b} granular behavioral data from the game Tom Clancy’s The Division (TCTD).
The workshop will be structured in three parts:
- UPEQ: structure of the questionnaire, constructs (competence, autonomy, relatedness and presence), factors and reliability
- TCTD dataset: overview of the game and detailed structure of the dataset
- Algorithm: explanation of the preference learning method based on support vector machines, to infer the mapping between gameplay and motivation.
Participants will have the chance of replicating the results of the paper; invert the prediction, inferring in-game behavior from player motivation or experiment with the method and dataset provided. We will conclude the session by sharing the results of the different workgroups.

Game AI Jam Facilitator and Guest Lecturer


Antonios Liapis

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


David Melhart

PhD Student and Research Support Officer at the Institute of Digital Games, University of Malta.

David Melhart is a PhD Student and Research Support Officer at the Institute of Digital Games, University of Malta. Under the supervision of Georgios Yannakakis and Antonios Liapis, he focuses his research on the application of affective computing in games user research and AI-assisted videogame design. He is responsible for the design and maintenance of paper media, web and social media presence, and online communication of the Summer School.

Summer School Program

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08:00-09:00 Registration
09:00-10:30 Introduction - AI that Plays
Georgios N. Yannakakis & Julian Togelius
10:30-11:00 Coffee Break
11:00-12:30 Deep RL and Games: AlphaStar Deep Dive
Tom Schaul
12:30-13:30 Lunch (Included with Registration)
13:30-15:00 AI in Strategy Games
Duygu Cakmak
Research Track Hands-On Workshop Track
15:00-16:30 Deep RL and Games: The Key Concepts
Tom Schaul
Using Unity and ML-Agents for Research, NPC AI, and More
Vincent-Pierre Berges
from 20:00 Welcome Reception
09:00-10:30 AI that Designs
Julian Togelius
10:30-11:00 Coffee Break
11:00-12:30 TBA
Oskar Stålberg
12:30-13:30 Lunch (Included with Registration)
13:30-15:00 TBA
Research Track Hands-On Workshop Track
15:00-16:30 Frontiers in PCG
Julian Togelius
09:00-10:30 AI that Experiences
Georgios Yannakakis
10:30-11:00 Coffee Break
11:00-12:30 TBA
12:30-13:30 Lunch (Included with Registration)
13:30-15:00 AI, Human Understanding and Empathy in Gaming
Daniel McDuff
Research Track Hands-On Workshop Track
15:00-16:30 Frontiers in Player Modeling
Georgios Yannakakis
Predicting Players’ Motivations from Gameplay Data
Ahmad Azadvar, Alessandro Canossa & David Melhart
09:00-10:30 TBA
10:30-11:00 Coffee Break
11:00-12:30 AI for Playtesting in Games
Sahar Asadi
12:30-13:30 Lunch (Included with Registration)
13:30-15:00 TBA
Game AI Jam
15:00-16:30 Introduction to the Game AI Jam
Antonios Liapis
09:00-12:30 Game AI Jam
12:30-13:30 Lunch (Included with Registration)
13:30-16:30 Game AI Jam

To get a better idea about our summer school program in general, be sure to check out last year's schedule!

Read About the 2018 School Program

Read About the 2019 School Program

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.


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The Summer School will be held at The Royal Danish Academy of Fine Arts, Schools of Architecture, Design and Conservation.


Copenhagen offers a wide variety of options for accommodation. While the inner city features high end hotels, outer areas (such as Amager or Norrebro) offer more budget options. Thanks to a number of metro and bus-lines, the city is well connected. As Copenhagen is a popular tourist destination in the summer, we advise you to book your accomodation in advance.

Popular hotels in Copenhagen:

Grand Hotel starting at € 178.50/night

Hotel Løven starting at € 168.62/night

For budget hotel options:

CPH Studio Hotel stating at € 97.83/night

Copenhagen Go Hotel stating at € 94.41/night

Hostels for students:

Generator Copenhagen starting at € 45.63/night

Copenhagen Downtown Hostel starting at € 37.67/night

Danhostel Copenhagen starting at € 30.59/night

Additionally, AirBnB listings in the area are starting from € 61/night

Please be aware that we cannot guarantee these prices and accomodation expected to get more expensive as we get closer to the summer school dates.


Copenhagen is a well-connected destination with a large ariport connecting to the inner city with a direct metro line. For those who travel on a budget, we can also recommend chekcing out the airport in Malmo, Sweden (only 60km from Copenhagen) a popular destination for budget airlines. Private coach companies arrange direct shuttle transfer between Malmo airport and central Copenhagen.

The city of copenhagen and the larger metro-area is services by three metro-lines, s-trains, and a reliable bus service. You can plan your travels and purchace tickets through the Rejseplanen app (for Android and Apple). Finally, you might consider renting a bike, while you are in town as Copenhagen is one of the most bike-friendly cities in the world!

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