Cooperation and competition : modeling intention and behavior in dual-agent interactions

A major goal of artificial intelligence research today is to build something that can cooperate with humans in an intelligent manner. In order to do so, we first must understand the mental mechanisms human use when solving problems of cooperation in dual-agent interactions, or between two people. We used reinforcement learning and Bayesian modeling to create a mathematical representation of this mental model. Our model is comprised of a high-level planner that understands abstract social intentions, and it employs two low-level planners that perform cooperative and competitive planning. To validate the model, we ran two experiments via Amazon Mechanical Turk to capture how humans attribute other players' behaviors and how they themselves behave in problems of cooperation such as the prisoner's dilemma. We compared our model against lesioned models and found that our model, which used both cooperative and competitive planning strategies, was the most representative of the data collected from both experiments.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Zhang, Lily, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Joshua B. Tenenbaum and Max Kleiman-Weiner.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119696

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119696