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.
|Title of host publication
|Proceedings of the 32nd International Conference on Automated Planning and Scheduling, ICAPS 2022
|Akshat Kumar, Sylvie Thiebaux, Pradeep Varakantham, William Yeoh
|Association for the Advancement of Artificial Intelligence
|Number of pages
|Published - 13 Jun 2022
|32nd International Conference on Automated Planning and Scheduling, ICAPS 2022 - Virtual, Online, Singapore
Duration: 13 Jun 2022 → 24 Jun 2022
|Proceedings International Conference on Automated Planning and Scheduling, ICAPS
|32nd International Conference on Automated Planning and Scheduling, ICAPS 2022
|13/06/22 → 24/06/22
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