Theoretical Study on Multi-objective Heuristic Search

Shawn Skyler, Shahaf Shperberg, Dor Atzmon, Ariel Felner, Oren Salzman, Shao-Hung Chan, Han Zhang, Sven Koenig, William Yeoh, Carlos Hernandez Ulloa

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence

Main Track. Pages 7021-7028.

https://doi.org/10.24963/ijcai.2024/776

PDF BibTeX

Abstract

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 their relation to traditional tie-breaking policies and provide theoretical findings. Finally, we study and empirically compare different ordering functions.

Keywords

  • Search: S: Heuristic search
  • Search: S: Other
  • Search: General
Welcome Future Conferences AI Journal Division IJCAI Proceedings Travel Grant Program IJCAI Awards