- Building agents that autonomously construct interpretable symbolic world models through interaction in VacuumWorld, a Python-based multi-agent simulation.
- Applying LLM-based autoformalization to convert raw observations into structured formal logical representations and transition models.
- Investigating whether learned world models improve agent reasoning and decision-making performance.
Python
VacuumWorld
LLMs
Formal Methods
Autoformalization