Second Pacific Asia Workshop on Game Intelligence & Informatics (GII)
May 11-14, 2021, Delhi, India
(To be held in conjunction with The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2021)
*** Submission Deadline Extended ***
- Updated Submission Deadline: January 31, 2021, 11:59 PM (PST)
- Accepting submission in Springer’s LNAI format. Please see Call For Papers and Submission Details
*** Currently Accepting Submissions ***
Schedule (to be announced)
Call for Papers: November 2, 2020
Submission Deadline: January 22, 2021, 11:59 PM (PST)
Updated Submission Deadline: January 31, 2021, 11:59 PM (PST)
Notification of Acceptance: February 22, 2021
Camera-ready Copies Due: March 8, 2021, 11:59 PM (PST)
Click here to see Call For Papers and Submission Details
Workshop Objectives and Scopes
The Game Intelligence and Informatics (GII) workshop aims to engage the larger data mining and AI community with discussions around the state of the art in machine learning and AI innovations for digital game data science, game user research and game design. We seek to highlight the unique challenges that lie in this domain and its niche subsets, and show how knowledge discovery and intelligent data analytics can help advance this domain. This workshop not only targets AI advances in gameplay, but also, more importantly, how AI and ML help provide predictive and prescriptive insights about game users that can thereby enable a personalized and adaptive player journey.
Understanding the game state and player action is a fundamental objective for any game researcher, because it sheds light on various aspects about the players and the game, such as skills, strategies, engagement, intention, retention, difficulty level and decision-making. Such intelligence helps us advance closer to the holy grail of providing a perfectly personalized and wholesome user experience, in a data-driven manner.
In recent times, there have been numerous innovative advances in the space of AI and machine learning for the gaming domain, including bots and reinforcement learning agents for perfect and imperfect information games, dynamic content generation for digital games, and so on. Similarly, the research community around human-computer interaction, has given us very interesting qualitative insights on the behavior of game users in terms of skill assessment and skill-matching, factors and designs that enhance engagement and retention, and so on. The recent proliferation of digital games for commercial, social, and educational purposes has further necessitated a new research direction towards game intelligence and knowledge discovery. The problem space becomes all the more intriguing when we consider the applications to and the challenges in niche gaming sectors such as (i) serious games – including education, citizen science, crowd-sourced task completion, etc.,(ii) MMORPG and other real-time, distributed multi-player games, (iii) AR/VR and IoT- enhanced experiential games, (iv) real-money games, including cards, casino and other partially observable stochastic games, and (v) esports and fantasy league games/sports. The key quest would be to enable a vision of end-to-end informatics around game dynamics, game platforms, and the players by consolidating orthogonal research directions of game AI, game data science, and game user research.
The immense volume of multi-dimensional data that digital games generate – including (but not limited to) game moves and actions of millions of players, click-stream in gaming platforms, data from wearables and sensors for AR/VR, and so on, contains a goldmine of information about the gameplay behavior, player inclinations, intentions, and preferences, etc. Many researchers have been tackling this data to develop insights about the user experience. The GII workshop aims to bring together research that lies at the intersection of machine learning, human-computer interaction, and digital gaming. We invite high-quality innovation that pushes the state of the art on topics including, but not limited to:
- Player modeling and behavior modeling
- Skill-based player journey – including skill assessment (which becomes especially difficult in stochastic games where there is always an element of chance involved), skill-based matching, predicting skill evolution
- Player intent mining and behavior prediction and timely predictions of events such as churn, addiction, etc.
- Player strategy mining
- Player profiling and persona detection
- Personalization of game and other content based on player persona, skill and journey
- Applications of game user research and machine learning in serious games (including education, citizen science, crowd-sourced task completion), stochastic games, fantasy sports, esports, AR/VR/wearables-enabled experiential games, massively multi-player distributed games, etc.
- Reinforcement learning agents that can enhance player experience via competition, up-skilling human players, assisting with strategies, etc.
- Adaptive procedural content generation, for customized content based on player persona, evolution, etc.