Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents …
This is an updated version of original (DOI: 10.26300/1pad-7574) that contains minor revisions (see page iv for list of revisions). Reinforcement learning defines the problem …
In the context of vast, complex, and dynamic social networks, strategic decision making relies on knowing how people are connected within their larger social communities. …
Humans are remarkably adaptive. Over the course of a day, year, and lifetime, we consistently encounter new and complex environments, each with their own goals …
This dissertation shows that qualitative and quantitative characterization of patterned structures in brain connectivity data obtained using diffusion MRI not only improves the exploration of …
Our capacity to swiftly and easily send digital images is an advancement unique to the last decade. Facilitated by the rise of online messaging platforms …
The purpose of this dissertation is to reevaluate the relationship between the aesthetics we call Romantic and the abstracting scientific procedures that have been underappreciated …
Generally intelligent agents must learn and plan in complex environments, with sensors and actuators that support various behaviors and tasks. This complexity hinders decision making, …