[Seminar] AlphaGo in Chemistry – Solving Real-World Problems by Means of Game AI Methods

On Tuesday 22nd May 2019 the Game AI Group will host a seminar by Mike Preuss from Universiteit Leiden

Title: AlphaGo in Chemistry – Solving Real-World Problems by Means of Game AI Methods
Speaker: Mike Preuss, Universiteit Leiden
Time: 4pm to 5pm, May 22, 2019
Room: 3.01, Bancroft Road Teaching Rooms, Mile End campus

All welcome (especially students), no pre-booking required.
The seminar will be followed by drinks in The Hub.

Monte Carlo Tree Search (MCTS) and Deep Neural Networks (DNN) in combination have pushed the limits of what artificial intelligence (AI) can do in areas where humans have been perceived as dominant over machines, as for the game Go. However, so far this has been limited to domains with simple and known rules, such as board games, where perfect world knowledge and cheap environment simulators are available. Unfortunately, this is usually not the case for real-world problems, usually we cannot obtain the rules themselves but only acquire knowledge from interactions with the world or simulation.

Chemical retrosynthesis (you know the product, but not how to get there) is one of such real-life domains with highly non-trivial, partially unknown rules. We show that this problem can very effectively be tackled with MCTS constrained by DNNs that learn from essentially the complete history of organic chemistry, as described in a recent Nature paper [1]. Our algorithm produces plans which human chemists could not tell apart from established plans taken from the literature. But it does not have to end here. We attempt to generalize the approach in order to make it applicable to other research areas as well.

Mike Preuss is Assistant Professor at LIACS, the computer science institute of Universiteit Leiden in the Netherlands. Previously, he was with ERCIS (the information systems institute of WWU Muenster, Germany), and before with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His main research interests rest on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization, and on computational intelligence and machine learning methods for computer games, especially in procedural content generation (PGC) and realtime strategy games (RTS).

[1] Segler, M. H., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604. https://www.nature.com/articles/nature25978

[Seminar] Evolutionary MCTS for Game AI and Beyond

On Tuesday 30th April 2019 the Game AI Group will host a seminar by Hendrik Baier from CWI Amsterdam

Title: Evolutionary MCTS for Game AI and Beyond 
Speaker:Hendrik Baier, CWI Amsterdam
Time: 11 am to 12 pm, Apr 30, 2019
Room: 3.02, Bancroft Road Teaching Rooms
All welcome (especially students), no pre-booking required.  After the talk we’re going for lunch with the speaker at Verdi’s.  Let me know if you’d like to join!
In this talk, I’m going to present a research direction that tries to combine the strengths of Monte Carlo Tree Search with those of Evolutionary Algorithms. Summarizing work presented at CIG and AIIDE last year, I’m first going to explain how Evolutionary MCTS (EMCTS) works, apply it to online planning in turn-based tactics/strategy games, and show how it can be extended to include a simple opponent model. Afterwards, I’m going to briefly go beyond the goal of “strongest possible AI” by showing first results on dynamic difficulty adaptation with EMCTS; and beyond the field of game AI altogether by discussing how I am applying RHEA, MCTS, and EMCTS in the context of managing flexibility on future energy markets!
I am currently living in the beautiful city of Amsterdam, working as researcher in the Intelligent and Autonomous Systems group at CWI. Before, I worked as research associate for artificial intelligence and data analytics at Digital Creativity Labs in York, England, and as research fellow in artificial intelligence in the Advanced Concepts Team of the European Space Agency in Noordwijk, Netherlands.  In 2015, I finished my Ph.D. in the Games and AI group at the Department of Knowledge Engineering, Maastricht University. My topic was planning and search — the search for an optimal strategy in a given problem, or just for a good next action to take. I used games as model problems. Application areas are both adversarial and collaborative scenarios with several agents, as well as optimization problems with a single agent.

[Seminar] Compressing Space, Warping Perception And Projecting Flowers Onto Cups Of Tea

OnWednesday 3rd April 2019 the Game AI Group will host a seminar by Michael Cook from Queen Mary University of London

Title: Compressing Space, Warping Perception And Projecting Flowers Onto Cups Of Tea
Speaker: Michael Cook, Queen Mary University of London
Time: 4pm to 5pm, Apr 3, 2019
Room: 1.04, Scape Building, Mile End Campus (building 64 on QMUL’s campus map.)
All welcome (especially students), no pre-booking required.


I’ll summarise some recent directions in my research on automated game design and procedural content generation, including two submissions to COG 2019.
* I’ve been looking at ways to compress state space graphs using the notion of reversible game actions, and why this might have exciting implications for game analysis in the future;
* Some new analytical techniques for procedural generators, allowing us to understand the behaviour of inputs to a generative system, and thus build tools for people which are easier to use.
* A new framework for studying AI that can play games for the purposes of curation, criticism and recommendation. I want to build AI that can appreciate games in ways other than just winning, and we’re hoping to organise a competition-like event around it to – we need your help!
Lastly, I’ll report on my visit to teamLab Borderless in Tokyo, a really inspirational experience that has made me rethink how we build digital experiences, and what the scope of our research can be.
Michael Cook is an AI researcher and game designer. He holds a Royal Academy of Engineering Research Fellowship at Queen Mary University of London, and is a Visiting Researcher at the Max Planck Institute for Software Systems in Kaiserslautern, Germany. His research interests include automated game design, computational creativity, procedural content generation and models of game design. He is best known as the designer of ANGELINA, a game-designing AI, and as the founder of PROCJAM, the procedural generation jam. In his spare time he designs games and writes about AI and game design. He once managed to collect every strawberry in Celeste.

You can find him on Twitter @mtrc, or find his papers, writing and more at possibilityspace.org

[Seminar] TextWorld – A framework for training reinforcement learning agents on text-based games

On Thursday 28th February 2019 the Game AI Group will host a seminar by Marc-Alexandre Côté from Microsoft Research Montreal

Title: TextWorld – A framework for training reinforcement learning agents on text-based games
Speaker: Marc-Alexandre Côté, Microsoft Research Montreal
Time: 11am to 12pm, Mar 28, 2019
Room: BR 4.02, Bancroft Road Teaching Rooms, QMUL Mile End Campus
All welcome (especially students), no pre-booking required.

Text-based games are complex, interactive simulations in which text describes the game state and players make progress by entering text commands. They are fertile ground for language-focused machine learning research. In addition to language understanding, successful play requires skills like long-term memory and planning, exploration (trial and error), and common sense.
The talk will introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to study generalization and transfer learning. This talk will also give an overview of the recent attempts to solve text-based games either using reinforcement learning or more handcrafted approaches.

Marc-Alexandre Côté is a researcher at Microsoft Research Montreal. His research focuses on machine learning, more specifically on generative models, reinforcement learning and language understanding. He is currently leading the TextWorld project, a learning environment for RL agent in the context of text-based games.

[Seminar] Building AI Dialogue Tools for Non-Technical Authors

On Wednesday 27th March 2019 the Game AI Group will host a seminar by Emily Short from Spirit AI.

Title: Building AI Dialogue Tools for Non-Technical Authors
Speaker: Emily Short, Spirit AI.
Time: 4pm to 5pm, Mar 27, 2019
Room: BR 3.02, Bancroft Road Teaching Rooms, QMUL Mile End Campus
All welcome (especially students), no pre-booking required.
This talk introduces Character Engine, a system for writing natural language or menu-based interactions with non-player characters, designed for a range of use cases from mobile gaming to voice-driven conversations with robots. We discuss the architecture of the system, the use of ML systems for understanding player intent, social models used to assist in contextual understanding of player input, and the application of more traditional game AI methods such as utility scoring and generative grammars to determine character responses. We also look at design features intended to support authors who might not themselves be experts in AI or NLP.


Emily Short is Chief Product Officer and an executive director at Spirit AI, where she works with natural language processing and generation, character moods and social behavior, and conversation modelling. She was previously the creative director of the Versu project, building interactive iPad stories around AI character agents. She has an extensive background in games writing and narrative design as well as tooling for writing interactive fiction. Her blog can be found at http://emshort.blog.

[Seminar] Generalisation of Simulation-Based Search for Autonomous Gameplaying

On Wednesday 6th February 2019 the Game AI Group will host a seminar by Alexander Dockhorn from Otto-von-Guericke University Magdeburg.

Title: Generalisation of Simulation-Based Search for Autonomous Gameplaying
Speaker: Alexander Dockhorn
Time: 4pm to 5pm, Feb 6, 2019
Room: BR 3.02, Bancroft Road Teaching Rooms, QMUL Mile End Campus
All welcome (especially students), no pre-booking required.

In my talk, I am going to present how various supervised learning mechanisms can lead to an approximation of an unknown game’s forward model, allowing agents to apply simulation-based search algorithms to general game learning tasks. The presentation will include an analysis of game model characteristics and the resulting development of the forward model approximation framework. Various forward model learning systems will be explored to highlight capabilities and limitations of the discussed framework.


I am PhD student at the Otto-von-Guericke University. My current research interests are computational intelligence in games and intelligent data analysis. My focus is on partial information games as well as learning rules, game models and playing strategies from previous experiences.

[Seminar] The Blurring of Video Games and Gambling: Daily Fantasy, Esports, Live Streaming, and Loot Boxes

On Tuesday 27th November the Game AI Group will host a seminar by Mark R Johnson from the University of Alberta.

Title: The Blurring of Video Games and Gambling: Daily Fantasy, Esports, Live Streaming, and Loot Boxes
Speaker: Dr Mark R Johnson
Time: 1pm-2pm (GMT), Nov 27, 2018
Room: BR 3.02, Bancroft Road Teaching Rooms, QMUL
This talk will examine the growing importance of gambling in modern video game design, the growing importance of video game design to contemporary gambling forms, and the overall wider interweaving of the two within the past five to ten years and their implications for the future of digital play. Firstly, the rapid rise and newfound near-ubiquity of Esports gambling, both “within” games (e.g. skin betting) and “outside” through third-parties (e.g. sports betting), poses new questions about the increasing extent to which Esports are coming to resemble traditional physical sports, and how consumption of Esports competition is shifting as corporate interests and “professionalisation” increase. Secondly, the paper will examine the expanding ongoing controversies surrounding “loot boxes”, the question of whether or not these are gambling, and how anti-“gambling” discourses are being mobilised in opposition, despite the primary issue players report being a question of paying to win. Thirdly, I will consider daily fantasy sports platforms, their commonalities with “sports management” video games, and their status as ambiguous gambling-gaming artefacts which subvert the clear boundaries between the two. Fourthly, the talk will examine online poker broadcasts on the live-streaming platform Twitch.tv, viewed by millions, and also how numerous live streamers are “gamblifying” methods by which their viewers can give them monetary support, using a suite of psychological techniques to encourage donations. The talk will conclude by emphasising the importance of these four phenomena for the future relationship between games and money, and how we might understand the growing role of gambling, and gambling-style systems, in many different kinds of digital play.
Mark R Johnson is a Killam Memorial Postdoctoral Fellow at the University of Alberta in Canada. His work focuses on the intersections between play and money, such as professionalised video game competition (E-sports), the live broadcast and spectating of video games on personalised online “channels”, and the blurring of video games and gambling in numerous contexts.

Outside academia, he is also an independent game developer, a regular games writer, blogger and podcaster, and a former professional poker player.

[Seminar] Perfect, Immortal Machines: The Future Of Automated Game Design

The Game AI Group at Queen Mary University of London has the pleasure to receive Dr. Micheal Cook for a seminar talk at 4pm on March 14th.
Get a ticket to secure your spot at the event!


Title: Perfect, Immortal Machines: The Future Of Automated Game Design
Speaker: Dr Michael Cook (http://www.gamesbyangelina.org/)
Time: 4pm-5pm (GMT), Mar 14, 2018
Room: Graduate Center 201, EECS, QMUL
Coffee/tea/cakes will be served at 3:30pm in the hub, wine and nibbles will be served in the hub after the seminar.

It’s a golden age for AI in games – they can play them, they can generate content for them, they can even design them. But what does this really mean for games culture? Is AI doomed to live in a dark room and only come out when we ask it to play Go?
In this talk, computational creativity researcher Michael Cook talks about the history of the future of automated game design; his latest work on his game designing AI ANGELINA; and explains how the future of AI isn’t a captive algorithm in a box, but an independent digital creative, with its own agenda, its own dreams, and its own Twitch channel.

Slides: http://www.gamesbyangelina.org/talks/qmul.pdf

[Seminar] “Combining Evolution and Learning” by Chrisantha Fernando (Google DeepMind)

The Game AI Group at Queen Mary University of London has the pleasure to receive Chrisantha Fernando for a seminar talk at 4pm on March 20th.
Get a ticket to secure your spot at the event!

Title: Combining Evolution and Learning
Speaker: Dr Chrisantha Fernando, Google DeepMind
Time: 4pm-5pm (GMT), Mar 20, 2018
Room: David Sizer Lecture Theater, Bancroft Building, EECS, QMUL
Drinks/reception: 5pm – 6pm – Informatics Hub, Computer Science Building

Chrisantha Fernando is currently a senior research scientist at Google DeepMind. He has previously been a lecturer at Queen Mary University of London, after having worked on models of the origin of life and Darwinian neurodynamics. He started his career as a medical doctor.


To find more information about the aspects presented in this talk, have a look at the links below:

[Seminar] “Bridge: a new challenge for AI?” by Dr. Véronique Ventos

Title: Bridge: a new challenge for AI?

Speaker: Dr. Véronique Ventos, Associate Professor at University of Paris-Saclay (France)

Date & Time: 4pm, 12th December 2017

Room: Eng3.24, Engineering Building, QMUL Mile End campus (building 15 on the campus map)

As usual, refreshments will be served before and after the seminar in the hub. Please register for helping preparing the refreshments.


Games have always been an excellent field of experimentation for the nascent techniques in computer science and in different areas of Artificial Intelligence (AI) including Machine Learning (ML). Despite their complexity, game problems are much easier to understand and to model than real life problems. Systems initially designed for games are then used in the context of real applications. In the last decades, designs of champion-level systems dedicated to a game (game AI) were considered as milestones of computer science and AI.
Go and Poker are the two most recent successes. In May 2017, AlphaGo (DeepMind) defeated by 3 to 0 the Go world champion Ke Jie. In January 2017, the Poker AI Libratus (Carnegie Mellon University) won a heads-up no-limit Texas hold’em poker event against four of the best professional players.
This success has not yet happened with regard to another incomplete information cards game, namely Bridge, which then provides a challenging problem for AI.
We think that Deep Learning (DL) cannot be the only AI future. There are many Machine Learning and more generally AI fields which can interact with DL. Bridge is a great example of an application needing more than black box approaches. The AlphaBridge project is dedicated to the design of a Bridge AI taking up this challenge by using hybrid framework in the field of Artificial Intelligence.
The first part of the webinar is devoted to the presentation of the different aspects of bridge and of various challenges inherent to it. In a second part, we will present our work concerning the optimization of the AI Wbridge5 developed by Yves Costel. This work is based on a recent seed methodology (T. Cazenave, J. Liu and O. Teytaud 2015, 2016) which optimizes the quality of Monte-Carlo simulations and which has been defined and validated in other games. The Wbridge5 version boosted with this method won the World Computer-Bridge Championship twice, in September 2016 and in August 2017. Finally, the last part is about various ongoing works related to the design of a hybrid architecture entirely dedicated to bridge using recent numeric and symbolic Machine Learning modules.


PhD in Artificial Intelligence (Knowledge Representation and Machine Learning) in 1997.
Associate professor at University Paris Saclay, France since 1998. Before joining in 2015 the group A&O in the interplay of Machine Learning and Optimization, she worked in the group LaHDAK (Large-scale Heterogeneous DAta and Knowledge) at Laboratory of Computer Science (LRI).
She started playing bridge in 2004 and is now 59th French woman player out of 48644 players.
In 2015, she set up the AlphaBridge project combining her two passions. AlphaBridge is dedicated to solve the game of bridge by defining a hybrid architecture including recent numeric and symbolic Machine Learning modules.


Useful links
If you don’t know Bridge and want to know how to learn it: http://www.learn2playbridge.com/
If you want to play Bridge online: https://www.bridgebase.com/