Intelligent agents typically operate in complex, dynamic environments. Such environments require that an agent be able to adapt to meet new demands. One promising strategy for such adaptation is to have an agent reason ...

This work investigates the origins of behavioral diversity in learning robot teams. Behavioral diversity refers to the extent to which agents assume distinct behavioral roles in a group. Most research in multi-robot teams ...

In the doctoral dissertation from which this chapter is drawn, Ashwin Ram presented an alternative perspective on the processes of story understanding, explanation, and learning. The issues that Ram explores in that ...

This thesis focuses on the classification of seizures, together with finding efficient and scalable ways to obtain high-quality datasets in order to train deep neural networks. It was motivated by the need to automate the ...

With continued advances in science and technology, big graph (or network) data, such as World Wide Web, social networks, academic collaboration networks, transportation networks, telecommunication networks, biological ...

The real world is complex, unstructured, and contains high levels of uncertainty. Although past work shows that robots can successfully operate in situations where a single skill is needed, they will need a framework that ...

This thesis aims to demonstrate how using 3D cues improves semantic labeling and object classification. Specifically, we will consider depth, surface normals, object classification, and pixel-wise semantic labeling in this ...

Mosquitoes are directly responsible for the death of more than a million people each year. Yet the ability to mitigate their deadly impact or even monitor them in the wild to better understand their behavior remains ...

Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to maximize goal attainment. However, reinforcement learning algorithms ...

In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, we focus on two ...