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  <title><![CDATA[PhD Proposal by Prithviraj Ammanabrolu]]></title>
  <body><![CDATA[<p><strong>Title:</strong>&nbsp;Language Learning in Interactive Environments</p>

<p>&nbsp;</p>

<p>Prithviraj Ammanabrolu<br />
Ph.D. Student<br />
School of Interactive Computing<br />
Georgia Institute of Technology</p>

<p><a href="http://prithvirajva.com">http://prithvirajva.com</a></p>

<p>&nbsp;</p>

<p><strong>Date:</strong> Friday, March 27<sup>th</sup>, 2020<br />
<strong>Time:</strong> 11:00 am to 1:00 pm (EST)<br />
<strong>Location:</strong> *<strong>No Physical Location</strong>*</p>

<p><strong>BlueJeans:</strong>&nbsp;<a href="https://bluejeans.com/7436488731/">https://bluejeans.com/7436488731/</a></p>

<p>&nbsp;</p>

<p><strong>Committee:</strong><br />
Dr. Mark Riedl (advisor), School of Interactive Computing, Georgia Institute of Technology</p>

<p>Dr. Charles Isbell, School of Interactive Computing, Georgia Institute of Technology<br />
Dr. Devi Parikh, School of Interactive Computing, Georgia Institute of Technology<br />
Dr. Matthew Hausknecht, Microsoft Research</p>

<p>&nbsp;</p>

<p><strong>Abstract:</strong></p>

<p>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&mdash;&rdquo;seeing&rdquo; and &ldquo;acting upon&rdquo; 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&mdash;each presenting its own set of unique challenges.</p>

<p>&nbsp;</p>

<p>Game-playing presents three challenges: (1) Knowledge representation&mdash;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&mdash;showcasing how structured, interpretable data representations in the form of knowledge graphs aid in each of these tasks&mdash;in addition to proposing how exactly these two aspects of Interactive Fiction can be combined to improve language learning across this board of challenges.</p>

<p>&nbsp;</p>
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