Senior Honors Projects, 2020-current

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Date of Graduation

5-7-2020

Document Type

Thesis

Degree Name

Bachelor of Science (BS)

Department

Department of Computer Science

Advisor(s)

Nathan Sprague

Abstract

Text-based games are a very promising space for language-focused machine learning. Within them are huge hurdles in machine learning, like long-term planning and memory, interpretation and generation of natural language, unpredictability, and more. One problem to consider in the realm of natural language interpretation is how to train a machine learning model to understand a text-based game’s objective. This work considers treating this issue like a machine translation problem, where a detailed objective or list of instructions is given as input, and output is a predicted list of actions. This work also explores how a supervised learning system might learn long-term planning and memory through the example of an oracle that always knows the best path. In this exploration, the work here shows that finding this best path is infeasible.

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