This project investigates how an intelligent agent can autonomously construct an interpretable model of its environment through interaction. The work will be conducted using VacuumWorld, a Python-based multi-agent simulation platform developed within the lab for studying agent behaviour in dynamic grid environments. In VacuumWorld, agents perceive a partially observable environment and act to achieve goals … full description “Learning Interpretable World Models in VacuumWorld via Autoformalization (available)”
Supervisor: Konstantinos Stathis
Active: updated 10-OCT-2022 (CHAA009) no expiry
Teaching Small LLMs to Reason (ongoing)
Large Language Models (LLMs) such as GPT4, are a game-changer for AI. Equipped with hundreds of billions of parameters, and trained on vast amounts of textual data totalling hundreds of terabytes, these models have revolutionised operations across numerous domains. But despite their considerable capabilities, their sheer size often means that they require substantial computational resources … full description “Teaching Small LLMs to Reason (ongoing)”