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Title: Language Learning in Interactive Environments
Prithviraj Ammanabrolu
Ph.D. Student
School of Interactive Computing
Georgia Institute of Technology
Date: Friday, March 27th, 2020
Time: 11:00 am to 1:00 pm (EST)
Location: *No Physical Location*
BlueJeans: https://bluejeans.com/7436488731/
Committee:
Dr. Mark Riedl (advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Charles Isbell, School of Interactive Computing, Georgia Institute of Technology
Dr. Devi Parikh, School of Interactive Computing, Georgia Institute of Technology
Dr. Matthew Hausknecht, Microsoft Research
Abstract:
Natural language communication has long been considered a defining characteristic of human intelligence. I am motivated by the question of how learning agents can understand and generate contextually relevant natural language in service of achieving a goal. In pursuit of this objective, I have been studying Interactive Fiction games, or text-adventures: simulations in which an agent interacts with the world purely through natural language—”seeing” and “acting upon” the world using textual descriptions and commands. These games are usually structured as puzzles or quests in which a player must complete a sequence of actions to succeed. My work studies two closely related aspects of Interactive Fiction: game-playing and game generation—each presenting its own set of unique challenges.
Game-playing presents three challenges: (1) Knowledge representation—an agent must maintain a persistent memory of what it has learned through its experiences with a partially observable world; (2) Commonsense reasoning to endow the agent with priors on how to interact with the world around it; and (3) Scaling to effectively explore combinatorially-sized natural language state-action spaces. On the other hand, game generation can be split into two complementary considerations: (1) World generation, or the problem of creating a world that defines the limits of the actions an agent can perform; and (2) Quest generation, i.e. defining actionable objectives grounded in a given world. I will present my work thus far—showcasing how structured, interpretable data representations in the form of knowledge graphs aid in each of these tasks—in addition to proposing how exactly these two aspects of Interactive Fiction can be combined to improve language learning across this board of challenges.
