Index Catalog // ScholarsArchive@OSU

Semantic embodied navigation : developing agents that navigate from language and vision

Creator:: Krantz, Jacob
Abstract:: Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...
Resource Type:: Dissertation
Full Text:: collaborations and Peter Anderson and Jesse Thomason for their advising insights and generosity. I thank the

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:: Dodge, Jonathan E.
Abstract:: Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
Resource Type:: Dissertation
Full Text:: taught me that learning is a great pastime; Andrew Anderson, my colleague in PhD trials and tribulations

Knowledge Base Completion Leveraging Natural Language Data

Creator:: Shahbazi, Hamed
Abstract:: Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
Resource Type:: Dissertation

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:: Koul, Anurag
Abstract:: Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
Resource Type:: Dissertation

A General Purpose Agent-Based Blackboard Optimization Method Applied to Sodium Fast Reactor Design and Analysis

Creator:: Stewart, Ryan H.
Abstract:: We present a novel multi-objective optimization methodology built upon a multi-agent blackboard framework. This multi-agent blackboard system (MABS) synthesizes blackboard architectures, multi-agent environments, and optimization theory. The blackboard architecture creates the framework for initializing, storing, and solving a multi-objective optimization problem. Multiple agents allow for an optimization problem to be...
Resource Type:: Dissertation

Incorporating and Learning Behavior Constraints for Sequential Decision Making

Creator:: Pinto, Jervis
Abstract:: Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
Resource Type:: Dissertation

Computational Approaches for RNA Structure Prediction with Dynamic Programming and Deep Neural Networks

Creator:: Deng, Dezhong
Abstract:: Our goal is to build a system to model the RNA sequences that reveals their structural information by using efficient dynamic programming algorithms and deep learning approaches. We aim to 1) achieve linear-time for RNA secondary structure prediction based on existing minimum free energy models; 2) utilize deep neural networks...
Resource Type:: Dissertation

Bayesian optimization with empirical constraints

Creator:: Azimi, Javad
Abstract:: Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
Resource Type:: Dissertation

Interpretable Machine Learning: Applications in Biology and Genomics

Creator:: Danaee, Padideh
Abstract:: Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
Resource Type:: Dissertation
Full Text:: couple of years. Special thanks to Dr. Christopher K. Mathews and Dr. Andy Karplus for their remarkable

ScholarsArchive@OSU

Semantic embodied navigation : developing agents that navigate from language and vision

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Knowledge Base Completion Leveraging Natural Language Data

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

A General Purpose Agent-Based Blackboard Optimization Method Applied to Sodium Fast Reactor Design and Analysis

Incorporating and Learning Behavior Constraints for Sequential Decision Making

Computational Approaches for RNA Structure Prediction with Dynamic Programming and Deep Neural Networks

Bayesian optimization with empirical constraints

Interpretable Machine Learning: Applications in Biology and Genomics

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