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Massachusetts Institute of Technology

Socially guided machine learning

Abstract

dc:description.abstract

Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people. Using a computer game, Sophie's Kitchen, an experiment with human subjects provides several insights about how people approach the task of teaching a machine. In particular, people want to direct and guide an agent's exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetric ways.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2006

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Thomaz, Andrea Lockerd
Advisor dc:contributor.advisor
  • Cynthia Breazeal.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/36160

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Thomaz, Andrea Lockerd. Socially guided machine learning. Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36160