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PhD Dissertation Defense - Taoan Huang
Thu, Aug 01, 2024 @ 03:30 PM - 05:30 PM
Thomas Lord Department of Computer Science
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Title: Improving Decision-Making in Search Algorithms with Machine Learning for Combinatorial Optimizations
Date and Time: August 1st, 2024: 3:30p - 5:30 p
Location: SAL 213
Committee Members: Sven Koenig, Bistra Dilkina, Jyotirmoy Deshmukh, Meisam Razaviyayn, Peter Stuckey
Abstract: Designing algorithms for combinatorial optimization problems (COP) is an important and challenging task 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-making, such as partitioning or pruning the search space and prioritizing exploration in the search space, is 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 frameworks to improve decision-making strategies in different 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.Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Taoan Huang
