Date of Award
Winter 2021
Project Type
Dissertation
Program or Major
Computer Science
Degree Name
Doctor of Philosophy
First Advisor
Wheeler Ruml
Second Advisor
Momotaz Begum
Third Advisor
Laura Dietz
Abstract
Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this dissertation, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.
Recommended Citation
Gu, Tianyi, "Metareasoning for Heuristic Search Using Uncertainty" (2021). Doctoral Dissertations. 2653.
https://scholars.unh.edu/dissertation/2653