Bridging Multi-Valued Heuristics and Dimensionality Reduction in Multi-Objective Search
Multi-objective shortest-path (MOSP) algorithms traditionally rely on single-valued heuristics (SVHs), which associate each state with a single admissible cost vector. While SVHs provide safe lower bounds, they fail to capture the trade-off structure of the Pareto frontier and often yield weak search guidance. Multi-valued heuristics (MVHs) address this limitation by mapping states to sets of cost estimates, enabling a richer approximation of possible trade-offs. Modern MOSP algorithms are highly dependent on dimensionality reduction (DR) techniques to efficiently perform dominance checks. However, integrating MVHs with DR introduces subtle correctness challenges. We show that naively com...
| Authors | Maya Wolff, Ariel Felner, Oren Salzman |
|---|---|
| Primary category | cs.AI |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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