  This paper provides a roadmap that explores the question of how to imbue
learning agents with the ability to understand and generate contextually
relevant natural language in service of achieving a goal. We hypothesize that
two key components in creating such agents are interactivity and environment
grounding, shown to be vital parts of language learning in humans, and posit
that interactive narratives should be the environments of choice for such
training these agents. These games are simulations in which an agent interacts
with the world through natural language -- "perceiving", "acting upon", and
"talking to" the world using textual descriptions, commands, and dialogue --
and as such exist at the intersection of natural language processing,
storytelling, and sequential decision making. We discuss the unique challenges
a text games' puzzle-like structure combined with natural language
state-and-action spaces provides: knowledge representation, commonsense
reasoning, and exploration. Beyond the challenges described so far, progress in
the realm of interactive narratives can be applied in adjacent problem domains.
These applications provide interesting challenges of their own as well as
extensions to those discussed so far. We describe three of them in detail: (1)
evaluating AI system's commonsense understanding by automatically creating
interactive narratives; (2) adapting abstract text-based policies to include
other modalities such as vision; and (3) enabling multi-agent and human-AI
collaboration in shared, situated worlds.
