Date(s) - 27/01/2023
11:00 am - 12:00 pm
Please note the change of time. The Seminar will now commence at 11.00am
Mini-Biography: Dr. André G. Pereira is a professor at the Federal University of Rio Grande do Sul, Brazil. His research aims to develop and explain the behavior of intelligent systems for sequential decision-making problems. Dr. Pereira has authored several papers on top-tier venues such as IJCAI, AAAI, and ICAPS. These papers contribute towards explaining the behavior of heuristic search algorithms, how to use combinatorial optimization-based reasoning to solve planning tasks, and how to use machine learning techniques to produce heuristic functions. Dr. Pereira is a program committee member of IJCAI and AAAI. His doctoral dissertation was awarded second place in the national Doctoral Dissertation Contest on Computer Science (2017), and first place in the national Doctoral Dissertation Contest on Artificial Intelligence (2018). Dr. Pereira advised three awarded students on national events, including first place and finalist in the national Scientific Initiation Work Contest (2018, 2022), and finalist in the national Master Dissertation Contest on Artificial Intelligence (2020).
Title: SICSA DVF Seminar – Understanding Neuro-Symbolic Planning
Venue: Heriot-Watt University
Abstract: In this seminar, we present the area of neuro-symbolic planning, introducing fundamental concepts and applications. We focus on presenting recent research on the problem of learning heuristic functions with machine learning techniques. We discuss the distinctions and particularities between the “model-based” and “model-free” approaches, and the different methods to address the problem. Then, we focus on explaining the behavior of “model-free” approaches. We discuss the generation of the training set, and present sampling algorithms and techniques to improve the quality of the training set. We also discuss how the distribution of samples over the state space of a task, together with the quality of its estimators, are directly related to the quality of the learned heuristic function. Finally, we empirically detail which factors have the greatest impact on the quality of the learned heuristic function.