Social inductive biases for reinforcement learning

How can we build machines that collaborate and learn more seamlessly with humans, and with each other? How do we create fairer societies? How do we minimize the impact of information manipulation campaigns, and fight back? How do we build machine learning algorithms that are more sample efficient when learning from each other's sparse data, and under time constraints? At the root of these questions is a simple one: how do agents, human or machines, learn from each other, and can we improve it and apply it to new domains? The cognitive and social sciences have provided innumerable insights into how people learn from data using both passive observation and experimental intervention. Similarly, the statistics and machine learning communities have formalized learning as a rigorous and testable computational process.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Adjodah, Dhaval D. K.(Adjodlah, Dhaval Dhamnidhi Kumar)

Advisor dc:contributor.advisor

• Alex "Sandy" P. Pentland.

• Program in Media Arts and Sciences

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128415

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128415