Structural Patterns Heuristics via Fork Decomposition
Structural Patterns Heuristics via Fork Decomposition
复制标题
通过分叉分解进行结构模式启发
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Carmel Domshlak
中科院分区：
文献类型：
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作者：
Michael Katz;Carmel Domshlak
We consider a generalization of the PDB homomorphism abstractions to what is called "structural patterns". The basic idea is in abstracting the problem in hand into provably tractable fragments of optimal planning, alleviating by that the constraint of PDBs to use projections of only low dimensionality. We introduce a general framework for additive structural patterns based on decomposing the problem along its causal graph, suggest a concrete non-parametric instance of this framework called fork-decomposition, and formally show that the admissible heuristics induced by the latter abstractions provide state-of-the-art worst-case informativeness guarantees on several standard domains.
