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New Results in Bounded-Suboptimal Search

AAAI 2022 · Learning theory and algorithms › Combinatorial and search optimization · approach: Heuristic and local search algorithms · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Alternate queues ordered by f, ˆf, and expected effortBounded-suboptimal search · IPC planning instances, w=1.5normalized coverage: 58.7 -> 62.5Helped
Alternate queues ordered by f, ˆf, and distance dBounded-suboptimal search · IPC planning instances, w=1.5normalized coverage: 58.7 -> 60.7Helped
Use expected-effort ordering without round-robin queue alternation (ablation)DXES · IPC planning domainscoverageHurt
Add round-robin alternation across queues to expected-effort search (ablation)DXES · IPC planning domainscoverageHelped
Reevaluate and requeue nodes when their xe estimate changes by more than 5% (ablation)CDXES · IPC planning domainscoverageHelped
Alternate focal search with the potential-ordered queue (ablation)Round-robin bounded-suboptimal search · Search benchmarksCPU timeHurt
Use a 95% probability threshold for focal-list inclusion (ablation)EES · Search benchmarksCPU timeNo clear effect
Require focal nodes to satisfy the admissible bound and expand the best-distance node whenever focal is nonempty (ablation)EES · Search benchmarksCPU timeMixed
Use two queues and expand from focal whenever nonempty (ablation)EES (Rushed EES) · Planning and search benchmarksperformanceHurt
Use a probabilistic expected-effort estimate to choose nodes, without round-robin scheduling (ablation)Bounded-suboptimal search · IPC planning domainscoverageHurt
Alternate only between the focal queue and best-f or best-ˆf, rather than using all three queues (ablation)Round-robin bounded-suboptimal search · Bounded-suboptimal searchperformanceHurt

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