Tue, Sep 26, 2023 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
PhD Thesis Proposal - Taoan Huang
Committee Members: Sven Koenig (co chair), Bistra Dilkina (co chair), Jyotirmoy Deshmukh, Stefanos Nikolaidis, John Carlsson, Peter Stuckey from Monash University
Title: Improving Decision Makings in Search Algorithms with Machine Learning for Combinatorial Optimizations
Abstract: Designing algorithms for combinatorial optimization problems (COP) are important and challenging tasks since it concerns a wide range of real world problems, such as vehicle routing, path planning and resource allocation problems. Most COPs are NP hard to solve and many research algorithms have been developed for them in the past few decades. Decision makings such as partitioning or pruning the search space and prioritizing exploration in the search space, are crucial to the efficiency and effectiveness of the search algorithms. Many of those heavily rely on domain expertise and human designed strategies.
In this thesis, we hypothesize that one can leverage machine learning to improve human designed decision making strategies in different categories of search algorithms for combinatorial optimization problems. We validate the hypothesis on the problems of multiagent path finding and solving mixed integer linear programs, introducing different machine learning techniques to advance a few state of the art optimal and heuristic search algorithms for the two problems.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa