Day 2 – September 3rd, 15:15-16:15
From Game Theory to AI in Games

Abstract

Agents are core entities existent in all games; from game theoretical abstract games to fully fledged complex digital games. How do these agents play these games? How can we model their play? How do we view their interactions with opponents (albeit they are friends or foes, humans or AIs)? To address the above questions, in this tutorial we use the principles of agent modeling and machine learning as overarching elements for connecting Game Theory to AI in digital games. The relationship between game theory and gameplay is strong but far from being explored extensively.
On the one hand, game theory studies mathematical models of rational decision makers within abstract games, for the analysis of economic or social behavior in adversarial or cooperative settings. More specifically, game theory focuses on characterizing or predicting the actions of rational or bounded-rational agents, and studies the related emerging “game solution concepts” such as the celebrated Nash equilibrium. On the other hand, popular board games such as Go and complex digital games such as StarCraft offer unique opportunities for the study of agent modeling, machine learning and artificial intelligence at large.
In this tutorial we attempt to incorporate foundational ideas and concepts from game theory and multiagent systems research in the game AI field. Particular game AI areas such as game-playing and player (or opponent) modeling could benefit from theoretical models of game-playing and empirical implementations of agent-based systems. Similarly, we argue that game AI research and practice can only help in advancing work on theoretical game theory and multiagent systems.
Interweaving these fields properly with the current stream of game AI research is a non-trivial exercise, given the different focus and paths these fields have taken. Further, there are limits to the degrees theoretical models can capture the complexity of games covered in game AI. These are the gaps our tutorial attempts to bridge by bringing successful examples of their interrelationship forward. Some instances of Game Theory that found successful applications in game AI include the various implementations of theoretical models for playing abstract, card and board games.

Tutors

Georgios Chalkiadakis & Georgios N. Yannakakis

Tutor additional information

The tutorial presenters are recognized experts in their fields of academic interest. In particular, Chalkiadakis, is a co-author (along with Michael Wooldridge and Edith Elkind) of the graduate level textbook Computational Aspects of Cooperative Game Theory (http://www.morganclaypool.com/doi/abs/10.2200/S00355ED1V01Y201107AIM016). Yannakakis, on the other hand, is a recognised figure within the AI and Games research community, co-author of the AI and Games textbook (gameaibook.org) and organiser of the AI and Games summer school series.


Georgios Chalkiadakis is an Associate Professor at the School of Electrical and Computer Engineering, Technical University of Crete (TUC). His research interests are in the areas of multi-agent systems, algorithmic game theory, and learning; and more specifically, in coalition formation, decision making under uncertainty, and reinforcement learning in multiagent domains. His PhD thesis (University of Toronto, 2007) was nominated for the IFAAMAS-2007 Victor Lesser Distinguished Dissertation Award. Before joining TUC, he was a Research Fellow at the School of Electronics and Computer Science, University of Southampton, UK; and has also worked as a software engineer at the Institute of Computer Science of the Foundation for Research and Technology – Hellas, and as a teacher of informatics in Greek high schools. Georgios has served in the Organizing Committee of several workshops in top AI and MAS international conferences; and has organised and chaired two AI/MAS summer schools. Georgios has been teaching multiagent systems and AI courses (undergraduate and graduate level) at TUC since 2011. Moreover, he has presented or co-presented tutorials on cooperative game theory at AAMAS-09, AAMAS-10, AAAI-10, AAMAS-2011, IJCAI-2011, AAMAS-2012, SETN-2016, EASSS-2017. More details on Georgios Chalkiadakis can be found at his web page: http://www.intelligence.tuc.gr/~gehalk

Georgios N. Yannakakis is a Professor and Director of the Institute of Digital Games, University of Malta, a co-founder of modl.ai and an Associate Professor at the Technical University of Crete. 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 200 papers and his work has been cited broadly (nearly 10,000 citations; h-index 51). He has attracted funding from several EU (mainly FP7, H2020) and national research projects 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 an tutorials in the most recognised conferences in his areas of research activity and has organized a few of the most respected conferences in the areas of game AI and game research. He has been an Associate Editor of IEEE Transactions on Computational Intelligence and AI in Games and the IEEE Transactions on Affective Computing (2009–2016). He is an Associate Editor of IEEE Transactions on Games. He has given tutorials in IEEE CIG (in 2008, 2011 and 2014), ACII (2009), IEEE GIC (2009) conferences and at the recent DeepLearn Summer School (2017). More details on Georgios N. Yannakakis can be found at his web page: http://yannakakis.net/