Humans teaching intelligent agents with verbal instruction

Krening, Samantha

Title:

Humans teaching intelligent agents with verbal instruction

dc.contributor.advisor	Feigh, Karen M.
dc.contributor.author	Krening, Samantha
dc.contributor.committeeMember	Riedl, Mark
dc.contributor.committeeMember	Isbell, Charles
dc.contributor.committeeMember	Chernova, Sonia
dc.contributor.committeeMember	Howard, Ayanna
dc.contributor.department	Aerospace Engineering
dc.date.accessioned	2019-05-29T14:02:24Z
dc.date.available	2019-05-29T14:02:24Z
dc.date.created	2019-05
dc.date.issued	2019-04-15
dc.date.submitted	May 2019
dc.date.updated	2019-05-29T14:02:24Z
dc.description.abstract	The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and intuitive for ordinary individuals to interact with. As part of a larger field of interactive machine learning (IML), this dissertation aims to create intelligent agents that can easily be taught by individuals with no specialized training, using an intuitive teaching method such as critique, demonstrations, or explanations. It is imperative for researchers to be aware of how design decisions affect the human’s experience because individuals who experience frustration while interacting with a robot are unlikely to continue or repeat the interaction in the future. Instead of asking how to train a person to use software, this research asks how to design software agents so they can be easily trained by people. When creating a robotic system, designers must make numerous decisions concerning the mobility, morphology, intelligence, and interaction of the robot. This dissertation focuses on the design of the interaction between a human and intelligent agent, specifically an agent that learns from a human’s verbal instructions. Most research concerning interaction algorithms aims to improve the traditional ML metrics of the agent, such as cumulative reward and training time, while neglecting the human experience. My work demonstrates that decisions made during the design of interaction algorithms impact the human’s satisfaction with the ML agent. I propose a series of design recommendations that researchers should consider when creating IML algorithms. This dissertation makes the following contributions to the field of Interactive Machine Learning: (1) design recommendations for IML algorithms to allow researchers to create algorithms with a positive human-agent interaction; (2) two new IML algorithms to foster a pleasant user-experience; (3) a 3-step design and verification process for IML algorithms using human factors; and (4) new methods for the application of NLP tools to IML.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/61232
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Robotics
dc.subject	Machine learning
dc.subject	Interactive machine learning
dc.subject	Human-agent interaction
dc.subject	Reinforcement learning
dc.subject	Natural language processing
dc.subject	Human-computer interaction
dc.subject	Human factors
dc.subject	Machine learning verification
dc.title	Humans teaching intelligent agents with verbal instruction
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Feigh, Karen M.
local.contributor.corporatename	College of Engineering
local.contributor.corporatename	Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries	Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication	43635977-32d3-4083-875f-9a9adff86a8f
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication	a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication	f6a932db-1cde-43b5-bcab-bf573da55ed6
thesis.degree.level	Doctoral