Browsing by Author "Sullivan, Keith"
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Item GeoMason: Geospatial Support for MASON(Department of Computer Science, George Mason University, 2010) Sullivan, Keith; Coletti, Mark; Luke, SeanMASON is a free, open-source Java-based discrete event multi-agent simulation toolkit that has been used to model network intrusions, unmanned aerial vehicles, nomadic migrations, and farmer/herder conflicts, among others. Many multi-agent models use georeferenced data which represent such things as road networks, rivers, vegetation coverage, population, and topology. However, MASON does not directly support georeferenced data. Therefore practitioners using MASON must hand craft such support, which may be difficult and error prone. In this paper we describe newly added geospatial functionality in MASON that addresses this problem. We discuss the design of this functionality, called GeoMASON, and its use and limitations. Finally, we give examples on how to import and manipulate georeferenced data.Item Hierarchical Multiagent Learning from Demonstration(2015) Sullivan, Keith; Sullivan, Keith; Luke, SeanDeveloping 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.