Index Catalog // ScholarsArchive@OSU

Creator:: Cerbone, G. (Giuseppe)
Abstract:: Many important application problems in engineering can be formalized as nonlinear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. For example, these methods depend critically on choosing a good starting point from which to perform the optimization search. In high-dimensional spaces, numerical...
Resource Type:: Dissertation
Full Text:: . Figure 2 shows an example problem in which L is the load and Si and S2 are two supports. The so-called

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
Full Text:: . Ryan H. Stewart, Author ACKNOWLEDGEMENTS I would like to begin by acknowledging my partner, Erika

Creator:: Hostetler, Jesse A.
Abstract:: Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
Resource Type:: Dissertation

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

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:: throughout a training cycle . . . . . . . . 177 L Data Appendix - CNNs trained to overfit to opponents

ScholarsArchive@OSU