TY - GEN
T1 - Theoretical Study on Multi-objective Heuristic Search
AU - Skyler, Shawn
AU - Shperberg, Shahaf
AU - Atzmon, Dor
AU - Felner, Ariel
AU - Salzman, Oren
AU - Chan, Shao Hung
AU - Zhang, Han
AU - Keonig, Sven
AU - Yeoh, William
AU - Ulloa, Carlos Hernandez
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - This paper provides a theoretical study on Multi-Objective Heuristic Search. We first classify states in the state space into must-expand, maybe-expand, and never-expand states and then transfer these definitions to nodes in the search tree. We then formalize a framework that generalizes A* to Multi-Objective Search. We study different ways to order nodes under this framework and its relation to traditional tie-breaking policies and provide theoretical findings. Finally, we study and empirically compare different ordering functions.
AB - This paper provides a theoretical study on Multi-Objective Heuristic Search. We first classify states in the state space into must-expand, maybe-expand, and never-expand states and then transfer these definitions to nodes in the search tree. We then formalize a framework that generalizes A* to Multi-Objective Search. We study different ways to order nodes under this framework and its relation to traditional tie-breaking policies and provide theoretical findings. Finally, we study and empirically compare different ordering functions.
UR - https://www.scopus.com/pages/publications/85204307437
M3 - Conference contribution
AN - SCOPUS:85204307437
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 7021
EP - 7028
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -