In 2012-2018 successful workshops on AI for adversarial real-time games were held at AIIDE in response to the considerable interest in the subject and the limited time for reporting on the annual StarCraft competition in the main AIIDE conference.
Last year, we'd like to broaden the workshop scope a bit to cover AI for all kinds of strategy games in the hope to attract more submissions and to spark discussions between research groups focusing on board game, real-time strategy game, and general video game AI.
The goal of this follow-up workshop is to again bring together AI researchers and game AI programmers from industry, who are interested in strategic game AI, to present and exchange ideas on the subject, and to discuss how academia and game companies can work together to improve the state-of-the-art in AI for games.
This one-day workshop will consist of paper presentations on strategy game related AI topics (listed below), game competition descriptions (StarCraft and mRTS), a commented man-machine StarCraft match (most likely replays), perhaps an invited presentation, and a discussion on future research. The competition summaries and results will be presented at the main AIIDE conference.
Contributions will be peer-reviewed to meet AAAI workshop standards.
This workshop welcomes original research contributions, position papers, competition AI system descriptions, and post-mortem game analyses in the area of AI for strategy games --- including modern video strategy games (such as FPS and RTS games), and turn based games and puzzles. Topics include, but are not restricted to:
Room: ??? 0855 :::Welcome 0900 Downing, Thompson, Bang: Automatically Solving Deduction Games via Symbolic Execution, Model Counting, and Entropy Maximization 0930 Kamlish, Chocron, McCarthy: SentiMATE: Learning to play Chess through Natural Language Processing 1000 Campbell, Churchill: Machine Learning State Evaluation in Prismata 1030 :::Break 1100 Huang, Ontanon: Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTS 1130 Partlan, Madkour, Jemmali, Miller, Holmgard, Seif El-Nasr: Player Imitation for Build Actions in a Real-Time Strategy Game 1200 :::Lunch 1330 Florian Richoux: Constrained optimization under uncertainty for decision-making problems: Application to microRTS 1400 Baek, Kim: Efficient Multi-Agent Reinforcement Learning for Many Agents 1430 Dave Churchill: StarCraft Tournament Report 1500 :::Break 1530 Santiago Ontanon: μRTS Tournament Report 1600 Research Directions Discussion 1700 :::Wrap-Up
The following people have agreed to help organizing the workshop: