Index Catalog // ScholarsArchive@OSU

Creator:: Wilson, Aaron
Abstract:: How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...
Resource Type:: Dissertation
Full Text:: . The role assignments for task j are governed by parameter φj . Learned roles are shared between tasks

Creator:: Ma, Chao
Abstract:: This thesis studies the problem of structured prediction (SP), where the agent needs to predict a structured output for a given structured input (e.g., Part-of-Speech tagging sequence for an input sentence). Many important applications including machine translation in natural language processing (NLP) and image interpretation in computer vision can be...
Resource Type:: Dissertation
Full Text:: of their occurrence in the document. Each output variable vij (i < j) corresponds to a coreferent

Creator:: Liere, Ray
Abstract:: We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...
Resource Type:: Dissertation

Creator:: Natarajan, Sriraam
Abstract:: Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...
Resource Type:: Dissertation

Creator:: Doppa, Janardhan Rao
Abstract:: We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
Resource Type:: Dissertation

Creator:: Raghavan, Aswin
Abstract:: Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
Resource Type:: Dissertation
Full Text:: Improvement (V 0n+1, π)← T (Vn) if ||V 0n+1 − Vn|| ≤ �(1− γ)/2γ return (Vn, π) //Partial Policy Evaluation j

ScholarsArchive@OSU