Webinar: Simulation-Based AI with LLMs

When: Friday, June 13, 2025, 3:00 PM - 4:00 PM
Where:

Abstract: 

Despite amazing progress in generative AI, even the largest and smartest large language models have serious and insurmountable limitations in their reasoning abilities. On the other hand, simulation-based AI agents make intelligent decisions based on the statistics of simulations using a forward model of a problem domain, providing a complementary type of intelligence. SBAI algorithms have very attractive properties, including instant adaptation to new problems, tunable intelligence and some degree of explainability. In this talk I outline some of the main algorithms in the area and discuss how combining them with the best features of LLMs can lead to new types of rapidly adaptive intelligence agents and demonstrate results on some interesting problems.

Speaker Biography: 

Simon Lucas is a full professor of AI in the School of Electronic Engineering and Computer Science at Queen Mary University of London where he leads the Game AI Research Group. He was previously Head of School of EECS at QMUL. He recently spent two years as a research scientist / software engineer in the Simulation-Based Testing team at Meta, applying simulation-based AI to automated testing.

Simon was the founding Editor-in-Chief of the IEEE Transactions on Games and co-founded the IEEE Conference on Games, was VP-Education for the IEEE Computational Intelligence Society and has served in many conference chair roles. His research is focused on simulation-based AI (e.g. Monte Carlo Tree Search, Rolling Horizon Evolution), bandit-based optimisation, and LLMs.

Register here.