FURI | Fall 2024
Synthesizing Interpretable Agents for Cybersecurity Contexts with Code Evolution of Augmenting Topologies
The objective is to create a model for evolving code that is more interpretable than conventional neural nets and can be used for the same reinforcement learning tasks. Researchers have been able to synthesize functions for test problems with basic integer operations. At a small scale, this demonstrates parity with existing neural-net-based solutions on simple tasks. Next steps will be moving on to bigger reinforcement learning tasks, and implementing more capabilities like external function calls and control flow. Interfacing with external functions would allow for solving cybersecurity-related tasks.
Student researcher
Alexander Ng
Computer science
Hometown: Los Altos, California, United States
Graduation date: Spring 2025