Digital Commons Network^™

Resource-Bounded Information Acquisition And Learning, Pallika H. Kanani

Open Access Dissertations

In many scenarios it is desirable to augment existing data with information acquired from an external source. For example, information from the Web can be used to fill missing values in a database or to correct errors. In many machine learning and data mining scenarios, acquiring additional feature values can lead to improved data quality and accuracy. However, there is often a cost associated with such information acquisition, and we typically need to operate under limited resources. In this thesis, I explore different aspects of Resource-bounded Information Acquisition and Learning.

The process of acquiring information from an external source involves …

Interactive Perception Of Articulated Objects For Autonomous Manipulation, Dov Katz

Open Access Dissertations

This thesis develops robotic skills for manipulating novel articulated objects. The degrees of freedom of an articulated object describe the relationship among its rigid bodies, and are often relevant to the object's intended function. Examples of everyday articulated objects include scissors, pliers, doors, door handles, books, and drawers. Autonomous manipulation of articulated objects is therefore a prerequisite for many robotic applications in our everyday environments. Already today, robots perform complex manipulation tasks, with impressive accuracy and speed, in controlled environments such as factory floors. An important characteristic of these environments is that they can be engineered to reduce or even …

Autonomous Robot Skill Acquisition, George D. Konidaris

Open Access Dissertations

Among the most impressive of aspects of human intelligence is skill acquisition—the ability to identify important behavioral components, retain them as skills, refine them through practice, and apply them in new task contexts. Skill acquisition underlies both our ability to choose to spend time and effort to specialize at particular tasks, and our ability to collect and exploit previous experience to become able to solve harder and harder problems over time with less and less cognitive effort.

Hierarchical reinforcement learning provides a theoretical basis for skill acquisition, including principled methods for learning new skills and deploying them during problem solving. …

Optimization-Based Approximate Dynamic Programming, Marek Petrik

Open Access Dissertations

Reinforcement learning algorithms hold promise in many complex domains, such as resource management and planning under uncertainty. Most reinforcement learning algorithms are iterative - they successively approximate the solution based on a set of samples and features. Although these iterative algorithms can achieve impressive results in some domains, they are not sufficiently reliable for wide applicability; they often require extensive parameter tweaking to work well and provide only weak guarantees of solution quality. Some of the most interesting reinforcement learning algorithms are based on approximate dynamic programming (ADP). ADP, also known as value function approximation, approximates the value of being …

Action-Based Representation Discovery In Markov Decision Processes, Sarah Osentoski

Open Access Dissertations

This dissertation investigates the problem of representation discovery in discrete Markov decision processes, namely how agents can simultaneously learn representation and optimal control. Previous work on function approximation techniques for MDPs largely employed hand-engineered basis functions. In this dissertation, we explore approaches to automatically construct these basis functions and demonstrate that automatically constructed basis functions significantly outperform more traditional, hand-engineered approaches. We specifically examine two problems: how to automatically build representations for action-value functions by explicitly incorporating actions into a representation, and how representations can be automatically constructed by exploiting a pre-specified task hierarchy. We first introduce a technique for …