In situations where a task can be cleanly formulated and data is plentiful, modern machine learning (ML) techniques have achieved impressive (and often super-human) results. Here, plentiful data'' can mean labels from humans, access to a simulator and well designed reward function, or other forms of interaction and supervision.<br><br>On the other hand, in situations where tasks cannot be cleanly formulated and plentifully supervised, ML has not yet shown the same progress. We still seem far from flexible agents that can learn without human engineers carefully designing or collating their supervision. This is problematic in many settings where machine learning is or will be applied in real world settings, where these agents have to interact with human users and may be used in settings that go beyond any initial clean training data used during system development. A key open question is how to make machine learning effective and robust enough to operate in real world open domains.<br><br>Artificial {\it open} worlds are ideal laboratories for studying how to extend the successes of ML to build such agents. <br>Open worlds are characterized by:<br>\begin{itemize}<br> \item Large (or perhaps infinite) collections of tasks, often not specified till test time; or lack of well defined tasks altogether (despite there being lots to do).<br> \itemunbounded'' environments, long ``epsiodes''; or no episodes at all.
\item Many interacting agents; more generally, emergent behavior from interactions with the environment.
\end{itemize}
On one hand, they retain many of the challenging features of the real world with respect to studying learning agents. On the other hand,
they allow cheap collection of environment interaction data. Furthermore, because many artificial worlds of interest are games that people enjoy playing, they could allow interaction with humans at scale.

A particularly promising direction is that open world games can bridge: the closed domains and benchmarks that have traditionally driven research progress and open ended real world applications in which resulting technology is deployed.
We propose a workshop designed to catalyze research towards addressing these challenges posed by machine learning in open worlds.
Our goal is to bring together researchers with a wide range of perspectives whose work focuses on, or is enabled by, open worlds. This would be the very first workshop focused on this topic, and we anticipate that it would play a key role in sharing experience, brainstorming ideas, and catalyzing novel directions for research.