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• W3136135338 abstract "This paper provides a roadmap that explores the question of how imbue learning agents with the ability 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 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 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 include other modalities such as vision; and (3) enabling multi-agent and human-AI collaboration in shared, situated worlds." @default.
• W3136135338 created "2021-03-29" @default.
• W3136135338 creator A5029637639 @default.
• W3136135338 creator A5061883150 @default.
• W3136135338 date "2021-03-18" @default.
• W3136135338 modified "2023-09-27" @default.
• W3136135338 title "Situated Language Learning via Interactive Narratives" @default.
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• W3136135338 hasPublicationYear "2021" @default.
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