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Hierarchical Multiagent Learning from Demonstration

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dc.contributor.advisor Luke, Sean Sullivan, Keith
dc.creator Sullivan, Keith 2015-07-29T18:42:48Z 2015-07-29T18:42:48Z 2015
dc.description.abstract Developing agent behaviors is often a tedious, time-consuming task consisting of repeated code, test, and debug cycles. Despite the difficulties, complex agent behaviors have been developed, but they required significant programming ability. An alternative approach is to have a human train the agents, a process called learning from demonstration. This thesis develops a learning from demonstration system called Hierarchical Training of Agent Behaviors (HiTAB) which allows rapid training of complex agent behaviors. HiTAB manually decomposes complex behaviors into small, easier to train pieces, and then reassembles the pieces in a hierarchy to form the final complex behavior. This decomposition shrinks the learning space, allowing rapid training. I used the HiTAB system to train George Mason University's humanoid robot soccer team at the competition which marked the first time a team used machine learning techniques at the competition venue. Based on this initial work, we created several algorithms to automatically correct demonstrator error.
dc.format.extent 188 pages
dc.language.iso en
dc.rights Copyright 2015 Keith Sullivan
dc.subject Computer science en_US
dc.subject Artificial Intelligence en_US
dc.subject Machine Learning en_US
dc.subject Multiagent Systems en_US
dc.subject Robotics en_US
dc.title Hierarchical Multiagent Learning from Demonstration
dc.type Dissertation en Doctoral en Computer Science en George Mason University en

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