The dissertation also describes three planning algorithms which plan by doing several Monte Carlo rollouts in the space of operational models. The best of these three planners, Plan-with-UPOM uses a UCT-like Monte Carlo Tree Search procedure called UPOM (UCT Procedure for Operational Models), whose rollouts are simulated executions of the actor's operational models. The dissertation also presents learning strategies for use with RAE and UPOM that acquire from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. The experimental results show that Plan-with-UPOM and the learning strategies significantly improve the acting e fficiency and robustness of RAE. It can be proved that UPOM converges asymptotically by mapping its search space to an MDP. The dissertation also describes a real-world prototype of RAE and UPOM to defend software-defined networks, a relatively new network management architecture, against incoming attacks.
Dean's rep: Dr. Pamela Abshire
Members: Dr. Jordan Boyd-Graber
Dr. Malik Ghallab
Sunandita Patra is a PhD candidate at the Department of Computer Science at the University of Maryland, College Park. Her research addresses the challenges faced by hierarchically organized actors (that do continual online planning and acting) in dynamic environments. She has developed integrated algorithms that incorporate all three: acting, planning, and learning using a representation of hierarchical operational models. Prior to this, she worked at Microsoft India Development center at Hyderabad. She did her B. Tech and M. Tech in Computer Science and Engineering from IIT Kharagpur and graduated in the year 2014. For more information, please visit: https://sunanditapatra.