Variable-Player Learning for Simulation-Based Games

Advisor: Bryce Wiedenbeck May 2020 - August 2021

We introduce a novel technique that uses a multi-headed neural network to analyze symmetric simulation-based games with a large, variable number of players, where the number of participants falls in a specified range. Our work extends prior work by converting what would otherwise be an extrinsic hyperparameter of a normal-form game into a dimension of the machine learning model. This allows a single model to learn patterns that generalize across a range of player values, which in turn enables types of analyses that compare or show robustness over the range of player values. We use this learned model to approximate robust equilibria, strategy profiles that represent reasonable predictions of behavior over a wide range of player counts. Finally, we present several possible measures of equilibrium robustenss, and compare their results in experiments on synthetic games.

AAAI-21 Undergraduate Consortium: PDF Poster
Davidson College Honors Thesis: PDF

Multi-SpooNN: A Lightweight Neural Network for Multiple Object Detection

Advisor: R. Iris Bahar May 2019 - August 2019

Real-time object detection is essential for autonomous robots to perform tasks in human environments. As a result, many autonomous robots rely on small, efficient neural networks. SpooNN is a lightweight convolutional neural network (CNN) for object detection that is optimized for FPGA implementation. However, the network only supports single object detection without classification. In this project, we extend the network capability to detect and classify multiple objects and then evaluate network performance on various datasets appropriate for autonomous robots.

DREU Program: Final Report
Grace Hopper Celebration: Poster