GAIG Game AI Research Group @ QMUL



The Game AI Group at Queen Mary University of London presented 12 papers, 1 demo and 1 tutorial at the IEEE Conference on Games 2020 (24-27 August 2020).


AI for Playing Games

Christopher Bamford and Simon Lucas
Neural Game Engine: Accurate learning of generalizable forward models from pixels

Ivan Bravi and Simon Lucas
Rinascimento: using event-value functions for playing Splendor

Raluca D. Gaina, Chiara F. Sironi, Mark H. M. Winands, Diego Perez Liebana and Simon Lucas
Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing

Diego Perez Liebana, Muhammad Sajid Alam and Raluca D. Gaina
Rolling Horizon NEAT for General Video Game Playing

Martin Balla, Simon Lucas and Diego Perez Liebana
Evaluating Generalization in General Video Game Playing

Alvaro Ovalle and Simon Lucas
Bootstrapped model learning and error correction for planning with uncertainty in model-based RL

Alexander Dockhorn and Simon Lucas
Local Forward Model Learning for GVGAI Games

Simon Lucas
Demo Paper: Cross Platform Games in Kotlin

Education and Games

Jack Ratcliffe and Laurissa Tokarchuk
Evidence for embodied cognition in immersive virtual environments using a second language learning environment

Game Design

Daniel Hernandez, Charles Takashi Toyin Gbadamosi, James Goodman and James Alfred Walker
Metagame Autobalancing for Competitive Multiplayer Games

Rokas Volkovas, Michael Fairbank, John Woodward and Simon Lucas
Practical Game Design Tool: State Explorer

PCG and AI for Game Design

Michael Cook
Procedural Generation and Information Games

Michael Cook
Software Engineering For Automated Game Design


Michael Cook
Getting Started in Automated Game Design

Aug 25th: 14:00-14:30 BST Joint Q&A live session for the tutorial