Computational Intelligence and Interactive GAmes Research

Computer games are an increasingly pervasive phenomenon in society. Ranging from casual games on the web, to social games on mobile phones, to console games with high-end 3D graphics, these highly interactive experiences provide a wealth of opportunities to examine computational, creative, critical thinking, and social processes. Additionally, games have many of the same complexities as the real world, while affording us, as researchers, the ability to conduct (relatively) tightly controlled scientific studies.

Because games are fundamentally an interactive medium, we consider them as a tool for studying people, their cognitive processes, decision making, and behavior. We seek to develop our understanding of how people behave in games, how the actions they take in games relate to cognitive and behavioral processes, and the effects of players' individual characteristics on the games they choose to play (and how they play them). Most of this work draws upon concepts from social and cognitive psychology as inspiration, but our primary tools remain AI and ML techniques.

Additionally, because games are a (relatively) low-cost way to study behavior and because analytics collected in games are generally vast, there is a unique opportunity to apply the tools of machine learning and data mining. With player populations in the 100s of thousands or more, commercial games companies can collect gigabytes upon gigabytes of data describing players' gameplay experiences. One of our approaches to research in this space is to leverage these massive data sets to gain insight into players' in-game behaviors. To do so, we design novel data mining and machine learning techniques. These algorithms can provide automated inference tools. More recently, we have sought to develop "semantically-interpretable" techniques that are capable not just of making accurate inferences about players, but also providing insight into why those inferences can be made. This semantically-interpretable quality helps us to learn about the qualities of play that make players unique, are indicative of cognitive processes, or explain players' motivations. These are just some high-level examples, we can learn significantly more from observations of humans playing computer games.

Another main thrust of our research agenda pertains to how games are authored. With many commercial games requiring 10s, if not 100s, of person-years of effort by highly-trained professionals to produce, there is a significant hurdle to creating most games. We believe that parts of the creative processes can be shared with computational processes. This sharing results in game experiences that are "co-created" as a partnership between designers, AI systems, and players' interactions. All of this work draws upon AI and ML methods (such as planning and statistical decision making methods), social psychology techniques (such as visual perception and influence), as well as HCI techniques (like interface design, and active preference elicitation algorithms).

This is a brief, very high-level, overview of what we do with games in the CIIGAR Lab. We do a lot more too. Please feel free to contact Dr. Roberts if you are interested in this work, have comments or suggestions, or would like to know more.

Projects