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DESCRIPTION:Thesis Proposal Committee Members:\n
Sven Koenig (Chair)\n
Satish Kumar Thittamaranahalli\n
Lars Lindemann\n
Satyandra Kumar Gupta\n
Ariel Felner\n
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Title: Speeding-up Multi-Objective Search Algorithms\n
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Abstract: In the Multi-Objective Search problem, given a graph in which each edge is annotated with a cost vector, a start state, and a goal state, a typical task is to compute a Pareto frontier. State-of-the-art multi-objective search algorithms conform to the same best-first algorithmic framework. These algorithms are similar to best-first search algorithms, such as A*, but, most differently, they need to consider multiple nodes (with costs that do not dominate each other) for the same state. Due to the similarity between multi-objective and single-objective search algorithms, I hypothesize that one can speed up multi-objective search algorithms by applying insights gained from single-objective search. More specifically, I propose to speed up multi-objective search algorithms by (1) sacrificing solution optimality, (2) using preprocessing techniques, and (3) using efficient data structures for dominance checks.
SEQUENCE:5
DTSTART:20231128T120000
LOCATION:EEB 110
DTSTAMP:20231128T120000
SUMMARY:Thesis Proposal (Han Zhang)
UID:EC9439B1-FF65-11D6-9973-003065F99D04
DTEND:20231128T130000
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