Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent work has introduced fork-decoupled search, addressing classical planning problems where a single center component provides preconditions for several leaf components. Given a fixed center path πC, the leaf moves compliant with πC can then be scheduled independently for each leaf. Fork-decoupled search thus searches over center paths only, maintaining the compliant paths for each leaf separately. This can yield dramatic benefits. It is empirically complementary to partial order reduction via strong stubborn sets, in that each method yields its strongest reductions in different benchmarks. Here  show that the two methods can be combined, in the form of strong stubborn sets for fork-decoupled search. This can yield exponential advantages relative to both methods. Empirically, the combination reliably inherits the best of its components, and often outperforms both.
['Daniel Gnad', 'Martin Wehrle', 'Jörg Hoffmann']
I am writing a research paper on a idea Decoupled strong stubborn sets
[{"from": "human", "value": "I am writing a research paper on a idea Decoupled strong stubborn sets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent work has introduced fork-decoupled search, addressing classical planning problems where a single center component provides preconditions for several leaf components. Given a fixed center path \u03c0C, the leaf moves compliant with \u03c0C can then be scheduled independently for each leaf. Fork-decoupled search thus searches over center paths only, maintaining the compliant paths for each leaf separately. This can yield dramatic benefits. It is empirically complementary to partial order reduction via strong stubborn sets, in that each method yields its strongest reductions in different benchmarks. Here  show that the two methods can be combined, in the form of strong stubborn sets for fork-decoupled search. This can yield exponential advantages relative to both methods. Empirically, the combination reliably inherits the best of its components, and often outperforms both."}]