Raluca D. Gaina is currently a Lecturer in Game AI at Queen Mary University of London, where she obtained her Ph.D. in Intelligent Games and Games Intelligence in May 2021 (in the area of rolling horizon evolution in general video game playing). She completed a B.Sc. and M.Sc. in Computer Games at the University of Essex in 2015 and 2016, respectively. In 2018, she did a 3-month internship at Microsoft Research Cambridge, working on the Multi-Agent Reinforcement Learning in Malmo Competition. She was the track organiser of the Two-Player General Video Game AI Competition 2016-2019 and was the Vice-Chair for Conferences of the IEEE CIS Games Technical Committee in 2020. Her research interests include general video game playing AI, evolutionary algorithms, and tabletop games.

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TALK

Tabletop Games and AI

Modern tabletop games are often very complex: featuring hundreds of game components, many players, social contexts, hidden information and uncertainty, dynamically changing rules and actions and much more. Learning to play only one game can take humans hours, and much more to fully grasp the concepts and strategies behind the game in order to master it. Learning to play all tabletop games? A very ambitious endeavour. In this talk, I will present our ongoing project on the Tabletop Games Framework (TAG), where we challenge AI to learn how to play all tabletop games instead. Even more so, I will discuss how AI can be used as tools to optimise automatic players or games, to gather statistics and aid in game development, and even to bring new interesting insights into existing games.