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< Back Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game Link ​ Author(s) J Goodman, S Lucas Abstract ​ More info TBA ​ Link

< Back Mind the gap: Distributed practice enhances performance in a MOBA game Link ​ Author(s) O Vardal, V Bonometti, A Drachen, A Wade, T Stafford Abstract ​ More info TBA ​ Link

< Back Studying General Agents in Video Games from the Perspective of Player Experience Link ​ Author(s) C Guerrero-Romero, S Kumari, D Perez-Liebana, S Deterding Abstract ​ More info TBA ​ Link

< Back ​ Dr Raluca Gaina Queen Mary University of London iGGi Outreach Coordinator iGGi Alum + Supervisor ​ ​ Dr 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 (MARLO). She was the track organiser of the Two-Player General Video Game AI Competition (GVGAI) 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. ​ r.d.gaina@qmul.ac.uk Email Mastodon https://rdgain.github.io/ Other links Website https://www.linkedin.com/in/raluca-gaina-347518114/ LinkedIn https://www.twitter.com/b_gum22 Twitter https://www.github.com/rdgain Github ​ Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games The n-tuple bandit evolutionary algorithm for automatic game improvement Population seeding techniques for rolling horizon evolution in general video game playing Automatic Game Tuning for Strategic Diversity Analysis of vanilla rolling horizon evolution parameters in general video game playing General video game for 2 players: Framework and competition General Video Game Artificial Intelligence Playing with evolution Rolling horizon evolutionary algorithms for general video game playing Self-adaptive rolling horizon evolutionary algorithms for general video game playing Rolling Horizon NEAT for General Video Game Playing Frontiers of GVGAI Planning Planning in GVGAI Efficient heuristic policy optimisation for a challenging strategic card game General video game artificial intelligence Optimising level generators for general video game AI 'Did you hear that?' Learning to play video games from audio cues Project Thyia: A forever gameplayer Tackling sparse rewards in real-time games with statistical forward planning methods General video game ai: A multitrack framework for evaluating agents, games, and content generation algorithms The Multi-Agent Reinforcement Learning in Malm\" O (MARL\" O) Competition VERTIGØ: visualisation of rolling horizon evolutionary algorithms in GVGAI General win prediction from agent experience League of Legends: A Study of Early Game Impact Self-adaptive MCTS for General Video Game Playing The 2016 two-player gvgai competition Introducing real world physics and macro-actions to general video game AI Rolling horizon evolution enhancements in general video game playing Learning local forward models on unforgiving games Themes Game AI - Previous Next

< Back Investigating uncertainty in digital games and its impact on player immersion Link ​ Author(s) S Kumari, C Power, P Cairns Abstract ​ More info TBA ​ Link

< Back Progress in Adaptive Web Surveys: Comparing Three Standard Strategies and Selecting the Best Link ​ Author(s) TM Prinz, J Plötner, M Croissant, A Vetterlein Abstract ​ More info TBA ​ Link

< Back Evaluating Generalization in General Video Game Playing Link ​ Author(s) D Perez-Liebana, M Balla, S Lucas Abstract ​ More info TBA ​ Link

< Back The Design Of" Stratega": A General Strategy Games Framework Link ​ Author(s) D Perez-Liebana, A Dockhorn, JH Grueso, D Jeurissen Abstract ​ More info TBA ​ Link

< Back Writing Retreat at Ammerdown 14 iGGi PGRs from QMUL, York, and Goldsmiths attended the three day writing retreat at Ammerdown near Bath at the end of March 2023. Everyone seemed to have a good time socially, but more importantly in their writing! This word cloud aptly summarise some of the feedback when PGRs were asked: “In one word please describe the Writing Retreat” Perceived primary benefits of attending included: Networking and focusing as a hive mind; Working together in a focused environment; Being away from home/university to provide time to focus; Increased motivation and less distraction because everyone was working at the same time; Bonding and understanding each other's writing journeys. Over 80% of attendees said that the networking and discussing matters with members of other CDTs was really useful. Over 80% also said getting away from their usual environment is effective in helping them think more clearly and/or (over 75%) more creatively. ​ Previous 3 Apr 2023 Next

< Back Rolling Horizon NEAT for General Video Game Playing Link ​ Author(s) D Perez-Liebana, MS Alam, RD Gaina Abstract ​ More info TBA ​ Link

< Back Automatic Game Tuning for Strategic Diversity Link ​ Author(s) Rory Davidson, Raluca Gaina, Rokas Volkovas, Carlos Gonzalez Diaz Abstract ​ More info TBA ​ Link

< Back ​ Guilherme Matos de Faria University of York ​ iGGi Alum ​ ​ I am a Portuguese student with a background in Artificial Intelligence. In 2016 I started attending video game tournaments and learned to analyse my matches and improve from it. When I did my masters in AI, I noticed that I could join my professional skills and my hobbies together to create something relevant to AI and competitive gaming. A description of James' research: I am looking to better understand which actions and decisions have the biggest impact on the outcome of a game. Currently, I am particularly focused on competitive turn based card games. What are the best players doing to win? How can players adapt to improve their chances of success? These are the questions I am hoping to help answer, giving players a better understanding of the game and how to improve. Please note: Updating of profile text in progress ​ Email Mastodon Other links Website LinkedIn Twitter Github ​ ​ Themes Game AI - Previous Next