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 - 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
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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
Y2 - 13 June 2022 through 24 June 2022
ER -