TY - GEN

T1 - A*pex

T2 - 32nd International Conference on Automated Planning and Scheduling, ICAPS 2022

AU - Zhang, Han

AU - Salzman, Oren

AU - Kumar, T. K.Satish

AU - Felner, Ariel

AU - Ulloa, Carlos Hernández

AU - Koenig, Sven

N1 - Funding Information:
The research at the University of Southern California was supported by the National Science Foundation (NSF) under grant numbers 1409987, 1724392, 1817189, 1837779, 1935712, and 2112533. The research was also supported by the United States-Israel Binational Science Foundation (BSF) under grant number 2021643 and Centro Nacional de Inteligencia Artificial CENIA, FB210017, BASAL, ANID. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or any government.
Publisher Copyright:
© 2022, Association for the Advancement of Artificial Intelligence.

PY - 2022/6/13

Y1 - 2022/6/13

N2 - In multi-objective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the component-wise sum of the cost vectors of the edges of the former path is “less than” the component-wise sum of the cost vectors of the edges of the latter path. The Pareto-optimal solution set is the set of all undominated paths from a given start vertex to a given goal vertex. Its size can be exponential in the size of the graph being searched, which makes multi-objective search time-consuming. In this paper, we therefore study how to find an approximate Pareto-optimal solution set for a user-provided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Pareto-optimal solution set, which enables the design of approximate multi-objective search algorithms that are efficient and produce small solution sets. We present such an algorithm in this paper, called A*pex. A*pex builds on PP-A*, a state-of-the-art approximate bi-objective search algorithm (where there are only two cost components) but (1) makes PP-A* more efficient for bi-objective search and (2) generalizes it to multi-objective search for any number of cost components. We first analyze the correctness of A*pex and then experimentally demonstrate its efficiency advantage over existing approximate algorithms for bi- and tri-objective search.

AB - In multi-objective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the component-wise sum of the cost vectors of the edges of the former path is “less than” the component-wise sum of the cost vectors of the edges of the latter path. The Pareto-optimal solution set is the set of all undominated paths from a given start vertex to a given goal vertex. Its size can be exponential in the size of the graph being searched, which makes multi-objective search time-consuming. In this paper, we therefore study how to find an approximate Pareto-optimal solution set for a user-provided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Pareto-optimal solution set, which enables the design of approximate multi-objective search algorithms that are efficient and produce small solution sets. We present such an algorithm in this paper, called A*pex. A*pex builds on PP-A*, a state-of-the-art approximate bi-objective search algorithm (where there are only two cost components) but (1) makes PP-A* more efficient for bi-objective search and (2) generalizes it to multi-objective search for any number of cost components. We first analyze the correctness of A*pex and then experimentally demonstrate its efficiency advantage over existing approximate algorithms for bi- and tri-objective search.

UR - http://www.scopus.com/inward/record.url?scp=85141285844&partnerID=8YFLogxK

U2 - 10.1609/icaps.v32i1.19825

DO - 10.1609/icaps.v32i1.19825

M3 - Conference contribution

AN - SCOPUS:85141285844

T3 - Proceedings International Conference on Automated Planning and Scheduling, ICAPS

SP - 394

EP - 403

BT - Proceedings of the 32nd International Conference on Automated Planning and Scheduling, ICAPS 2022

A2 - Kumar, Akshat

A2 - Thiebaux, Sylvie

A2 - Varakantham, Pradeep

A2 - Yeoh, William

PB - Association for the Advancement of Artificial Intelligence

Y2 - 13 June 2022 through 24 June 2022

ER -