Senior Honors Projects, 2020-current
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Date of Graduation
5-7-2020
Document Type
Thesis
Degree Name
Bachelor of Science (BS)
Department
Department of Computer Science
Advisor(s)
Nathan R. Sprague
David H. Bernstein
Kevin Molloy
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.
Recommended Citation
Snarr, Anthony, "Towards natural language understanding in text-based games" (2020). Senior Honors Projects, 2020-current. 70.
https://commons.lib.jmu.edu/honors202029/70
