Date of Award

2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Josh Bongard

Abstract

Distributed human computing techniques have been shown to be effective ways of accessing the problem-solving capabilities of a large group of anonymous individuals over the World Wide Web. They have been successfully applied to such diverse domains as computer security, biology and astronomy. The success of distributed human computing in various domains suggests that it can be utilized for complex collaborative problem solving. Thus it could be used for "machine science": utilizing machines to facilitate the vetting of disparate human hypotheses for solving scientific and engineering problems.

In this thesis, we show that machine science is possible through distributed human computing methods for some tasks. By enabling anonymous individuals to collaborate in a way that parallels the scientific method -- suggesting hypotheses, testing and then communicating them for vetting by other participants -- we demonstrate that a crowd can together define robot control strategies, design robot morphologies capable of fast-forward locomotion and contribute features to machine learning models for residential electric energy usage. We also introduce a new methodology for empowering a fully automated robot design system by seeding it with intuitions distilled from the crowd.

Our findings suggest that increasingly large, diverse and complex collaborations that combine people and machines in the right way may enable problem solving in a wide range of fields.

Language

en

Number of Pages

178 p.