Games and Tasks


The concept of a "task" is fundamental for Visual Analytics and ubiquitous within games. Both playing a game and analyzing data involves performing tasks on abstract representations of knowledge. One of our earliest examples of graph drawing comes from a book about games, depicting a board of the Morris game (Fig. 1). On the high-skill level of many games it becomes clear that there are core cognitive processes related to decision making, combinatorial analysis, visual perception, and eye-hand coordination involved in winning. Furthermore, many processes that visual analytics intends to illuminate through exploring the data can be mathematically modeled as games. The game Sokoban, for instance, is equivalent to the problem of agents moving boxes in a warehouse. Chess mastery is about exploring future deterministic states efficiently, and poker involves estimating risks and probabilities with incomplete data.

Studying the relationship between games, tasks, and data analysis provides a rich ground for research in Human Computer Interaction. There is a big trans disciplinary field of research condensing around such topics due to the rise of machine learning. DeepMind has declared that Hanabi, a cooperative card game is the next frontier in AI Research. In the future, the distinctions between types of applications and technologies will become more blurry as all tasks will converge to collaborating with autonomous agents of varying "intelligence".


The aim of this theme is to explore the connections between games and visual analytics through abstract tasks.

There are many topics that fit within the scope of this theme. Some example research questions are:

  •  How to generalize visual tasks in games as visual analytics tasks?
  •  How to design games for evaluating visual analytics systems?
  •  How to visualize game data, and what insights can be gained from it?
  •  How to use machine learning to visualize and navigate through solution spaces of games?

Other information

The links in the problem description contain papers, videos, and research material that can serve as a starting point or inspiration for applicants.


Further information

visual analytics, games and game theory, machine learning, human computer interaction, evaluation methodologies
Data Quality
Information Extraction (IE) and Transformation
Information Visualization (IV)
Plan Execution
Visual Analytics (VA)
Previous knowledge
There is no specific required knowledge for this topic, as it covers a broad scope of topics.