Artificial Agents Use Reinforcement Learning to Explain Actions, a Necessary Step as They Get Smarter
By Nathan Deen
Discovering something new and unexpected is a dream for any researcher who studies artificial intelligence (AI), but it doesn’t happen often.
But that’s exactly what happened to School of Interactive Computing Professor Mark Riedl and his collaborators, Xiangyu Peng and Prithviraj Ammanabrolu, when they were building a reinforcement learning model for an AI they created to play text adventure games.
AI agents typically learn computer gaming through reinforcement learning, which teaches them how to solve problems that require multiple steps. In text adventure gaming, the agent is presented with a problem scenario and must use text responses to solve the problem.
Text-adventure games were created more than 45 years ago. Riedl said he looked back to them because contemporary games often prove to be limiting for an AI that’s being taught through reinforcement learning.