Conferences

Knowledge Extraction from Games (KEG) is a workshop exploring questions of and approaches to the automated extraction of knowledge from games. We use “knowledge” in the broadest possible sense, including but not limited to design patterns or insights, game rules, character graphics, environment maps, music and sound effects, high-level goals, meaning or readings of the game, transferable skills, aesthetic standards and conventions, or abstracted models of games.

It includes and expands on the mandate of a recent vision paper at Computational Intelligence in Games (CIG 2017), Automated Game Design Learning.

Important Dates:

November 12: Submissions due to organizers (via EasyChair)
November 26: Notifications to authors
December 8: Return camera-ready papers to organizers
January 27/28: Workshop (Exact day TBD)
Games can be understood as simplified models of aspects of reality. They therefore provide useful structuring information for reasoning tasks and provide interesting environments for knowledge extraction and specification recovery--environments like video games, board games, and informal simulations of reality. For example, tasks like quadcopter control and stock market analysis can be understood as games.

Some examples of work that would be appropriate for KEG include:

• Contextual query-answering in games where non-player characters (or visual cues in environment design) offer hints to solve problems
• Extracting architectural information from game level layouts
• Transfer learning, analogical reasoning, or goal reasoning within or between games or game levels
• Game-playing agents which can explain their own actions or policy in terms of the game's rules
• Learning the rules of a game from observation, or learning higher-level rules or goals automatically
• Determining a designer or player's mental model of game rules, and whether that differs from the rules induced by the game's implementation

https://sites.google.com/view/kegworkshop/home?authuser=0