Doctoral Thesis: Balancing Exploration and Exploitation: Loss-Targeted Exploration for Scientific Decision-Making
32-G882 (Hewlett Room)
Abstract: How do we collect observational data that reveal fundamental properties of scientific phenomena? This is a key challenge in modern scientific decision-making. Scientific phenomena are complex—they have high-dimensional and continuous state, exhibit chaotic or complex dynamics, and generate incomplete or noisy sensor observations. This thesis argues that autonomous decision-making in real-world, scientific domains requires loss-targeted exploration—exploration strategies that are tuned to a specific task or loss function. By explicitly quantifying the change in task performance due to exploratory actions, we enable decision-makers that explore parsimoniously and contend with highly uncertain real-world environments. We develop novel algorithms for loss-targeted exploration in partially observable Markov decision processes (POMDPs) and online learning problems. These methods are motivated by and applied to real-world scientific applications, including robotic deep sea hydrothermal plume discovery and climate and weather forecasting. This thesis demonstrates that autonomous decision-making can enhance human scientific discovery, placing sensors in the right place at the right time to validate a hypothesis or collect a critical observation.
- Date: Thursday, July 14
- Time: 10:00 am
- Category: Thesis Defense
- Location: 32-G882 (Hewlett Room)